A Volunteer Passion: A Qualitative Look at How We Measure and Reward the Work of Medical Educators.

Background  Medical educators face many challenges, including the absence of defined roles, lack of standard career paths, and limited support in systems that generally prioritize research and clinical productivity over educational activities. Providers also teach to widely varying degrees. This study was designed to specifically examine the professional rewards and obstacles experienced by physicians who have dedicated significant energy and career focus to medical education. Methodology A phenomenological approach was used in this qualitative study. Purposeful sampling was utilized to identify medical educators from different institutions and geographical areas. Participants were categorized by gender and career stage. Semi-structured interviews were conducted, and reflexive thematic analysis was used to develop themes across items and participants. Results Twenty-two medical educators were interviewed (11 males, 11 females), with an average age of 51 (range: 38-72) years. The average time from completion of training was 18 years (range: <1 to 41 years). Two main themes were constructed, which related to medical educators' career motivations and challenges: (1) Joy and purpose (subthemes: Interaction with learners, Impact, and Innovation) and (2) Everyone teaches (subthemes: Lack of recognition, Lack of reward, Malalignment of metrics) Conclusions The greatest source of motivation and satisfaction for medical educators is linked to the work itself; in addition to interactions with learners, educators derive pleasure from the innovation, collaboration, and systems thinking involved in their work. Importantly, participants also experience dissatisfaction, primarily due to a lack of recognition and reward, and metrics that do not consistently demonstrate their achievements. Participants provided examples of metrics that more accurately reflected the work of education; they identified clear benefits of academic promotion; and they highlighted significant challenges in the promotional system. The implementation of appropriate systems of measurement and reward is needed to better support the work of medical educators. Our aim should be not only to increase opportunities for satisfaction but also to reduce factors that cause frustration and limit advancement.

Linking animal and human health burden: challenges and opportunities.

Quantifying the impact of poor animal health outcomes on human health represents a complex challenge. Using the disability-adjusted life year (DALY) metric as an endpoint, this article discusses how animal health outcomes can impact humans through three key processes: directly through zoonotic disease, indirectly via changes in yields and their impacts on nutrition and wealth, and finally, through indirect features associated with the agricultural industry, such as pharmaceuticals and climate change. For each process, the current state of the art and feasibility of global DALY-associated estimates are discussed. Existing frameworks for zoonoses already consider some key pathogens; ensuring completeness in the pathogens considered and consistency in methodological decisions is an important next step. For diet, risk factor frameworks enable a calculation of attributable DALYs; however, significant economic methodological developments are needed to ensure that local production changes are appropriately mapped to both local and global changes in dietary habits. Concerning wealth-related impacts, much work needs to be done on method development. Industry-related impacts require a focus on key research topics, such as attribution studies for animal antimicrobial resistance contributing to human outcomes. For climate change, a critical next step is identifying to what extent associated industry emissions are amenable to change should animal health outcomes improve. Allocation of finite funds to improve animal health must also consider the downstream impact on humans. Leveraging DALYs enables comparisons with other human health-related decisions and would represent a transformative way of approaching animal health decision-making should the obstacles in this article be addressed and new methods be developed.

La quantification de l'impact des problèmes de santé animale sur la santé humaine constitue un défi d'une grande complexité. En se servant de l'indicateur des années de vie ajustées sur l'incapacité (DALY) comme critère d'évaluation, les auteurs examinent trois processus essentiels illustrant l'impact que la situation zoosanitaire peut avoir sur la santé humaine : impact direct résultant des maladies zoonotiques, impact indirect résultant des mauvaises performances des animaux et de leurs conséquences sur la nutrition et la création de richesses, et enfin, effets indirects résultant de facteurs en lien avec le secteur agricole, par exemple l'utilisation de produits pharmaceutiques et le changement climatique. Pour chacun de ces processus, les auteurs font le point sur l'état actuel des connaissances et sur l'applicabilité des évaluations mondiales basées sur l'indicateur DALY. Les cadres existants relatifs aux zoonoses recouvrent déjà certains agents pathogènes majeurs ; la prochaine étape importante consistera à assurer une couverture complète des agents pathogènes et à veiller à la cohérence des décisions méthodologiques. S'agissant de l'alimentation, les cadres basés sur l'analyse des facteurs de risque permettent de calculer les DALY imputables à l'alimentation ; toutefois, d'importantes avancées méthodologiques sur les aspects économiques de cette corrélation seront nécessaires pour s'assurer que tout changement intervenant localement en matière de production animale est correctement mis en correspondance avec les modifications des habitudes alimentaires dans ce même contexte local mais aussi à l'échelle mondiale. S'agissant des impacts liés à la création de richesses, il reste beaucoup à faire dans le domaine méthodologique. La détermination des impacts liés aux filières d'élevage requiert des travaux axés sur des sujets précis, par exemple des études visant à déceler les sources de la résistance aux agents antimicrobiens qui contribuent à l'apparition d'antibiorésistances chez l'être humain. Enfin, pour ce qui concerne le changement climatique, une étape cruciale consistera à déterminer dans quelle mesure les émissions associées à l'élevage sont susceptibles de changer en cas d'amélioration de la situation zoosanitaire. Dans un contexte de ressources limitées, l'affectation de fonds à l'amélioration de la santé animale doit également prendre en compte l'impact en aval sur la santé humaine. L'utilisation de l'indicateur DALY permet des comparaisons avec d'autres décisions de santé publique et représenterait une approche transformative de la prise de décision en santé animale, dès lors que les obstacles mentionnés dans cet article sont surmontés et que de nouvelles méthodes sont mises au point.

Cuantificar el impacto de una mala sanidad animal en la salud humana es un desafío complejo. Utilizando el parámetro de años de vida ajustados en función de la discapacidad (AVAD o DALY) como criterio de valoración, en este artículo se examina cómo la sanidad animal puede repercutir en los seres humanos a través de tres procesos clave: directamente, a través de las zoonosis; indirectamente, a través de cambios en los rendimientos y sus repercusiones en la nutrición y la riqueza; y, por último, a través de factores indirectos asociados a la industria agropecuaria, como los fármacos y el cambio climático. Para cada uno de estos procesos, se examinan el estado actual y la viabilidad de estimar AVAD a escala mundial. Los marcos existentes para la zoonosis ya tienen en cuenta algunos patógenos claves; garantizar la exhaustividad de los patógenos considerados y la coherencia en las decisiones metodológicas es un próximo paso importante. En lo que respecta a la alimentación, aunque los marcos de factores de riesgo permiten calcular los AVAD atribuibles, se necesitan importantes avances metodológicos en el ámbito económico para asegurar que los cambios en la producción local se correspondan adecuadamente con los cambios locales y mundiales en los hábitos alimentarios. En cuanto a las repercusiones en la riqueza, queda mucho trabajo por hacer en el desarrollo de métodos. Para abordar las repercusiones relacionadas con la industria, es necesario centrarse en temas clave de investigación, como los estudios de atribución relativos al impacto en la salud humana de la resistencia a los antimicrobianos en los animales. En lo que se refiere al cambio climático, un próximo paso crucial es determinar en qué medida las emisiones de la industria podrían cambiar, en función de la mejora de los resultados en materia de sanidad animal. Al asignar fondos limitados para la mejora de la sanidad animal también se deben tener en cuenta las repercusiones correspondientes en los seres humanos. Utilizar los AVAD permite hacer comparaciones con otras decisiones importantes relacionadas con la salud humana y representaría una forma transformadora de enfocar la toma de decisiones en materia de sanidad animal, en caso de que se aborden los obstáculos presentados en ese artículo y se desarrollen nuevos métodos.

Cardiac Surgical Bleeding, Transfusion and Quality Metrics: Joint Consensus Statement by the Enhanced Recovery After Surgery Cardiac Society and Society for the Advancement of Patient Blood Management.

BACKGROUND: Excessive perioperative bleeding is associated with major complications in cardiac surgery, resulting in increased morbidity, mortality and cost. METHODS: An international expert panel was convened to develop consensus statements on the control of bleeding and management of transfusion, and to suggest key quality metrics for cardiac surgical bleeding. The panel reviewed relevant literature from the previous 10 years and used a modified RAND Delphi methodology to achieve consensus. RESULTS: Thirty consensus statements in 8 categories were developed, including prioritizing control of bleeding, pre-chest closure checklists and the need for additional quality indicators beyond re-exploration rate, such as time to re-exploration. Consensus was also reached on the need for a universal definition of excessive bleeding, the use of antifibrinolytics, optimal cessation of antithrombotic agents, and preoperative risk scoring based on patient and procedural factors to identify those at greatest risk of excessive bleeding. Furthermore, there is a need for an objective bleeding scale based on the volume and rapidity of blood loss accompanied by viscoelastic management algorithms and standardized, patient-centered blood management strategies reflecting an interdisciplinary approach to quality improvement. CONCLUSIONS: Prioritizing the timely control and management of bleeding is essential to improving patient outcomes in cardiac surgery. To this end, a cardiac surgical bleeding quality metric that is more comprehensive than re-exploration rate alone is needed. Similarly, interdisciplinary quality initiatives that seek to implement enhanced quality indicators will likely lead to improved patient care and outcomes.

Interventions to optimize dispatcher-assisted CPR instructions: A scoping review.

Aim: To review and summarize existing literature and knowledge gaps regarding interventions that have been tested to optimize dispatcher-assisted CPR (DA-CPR) instruction protocols for out-of-hospital cardiac arrest (OHCA). Methods: This scoping review was undertaken by an International Liaison Committee on Resuscitation (ILCOR) Basic Life Support scoping review team and guided by the ILCOR methodological framework and the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR). Studies were eligible for inclusion if they were published in peer-reviewed journals and evaluated interventions used to improve DA-CPR. The search was carried out in MEDLINE, EMBASE, Education Resources Information Center (ERIC), PsycINFO, the Cochrane Library, Evidence Based Medicine (EBM) Reviews, and the Campbell Library from 2000 to December 18, 2023. Results: After full text review, 31 studies were included in the final review. The interventions reviewed were use of video at the scene (n = 9), changes in terminology about compressions (n = 6), implementation of novel DA-CPR protocols (n = 4), advanced dispatcher training (n = 3), centralization of the dispatch center (n = 2), use of metronome or varied metronome rates (n = 2), change in CPR sequence and compression ratio (n = 1), animated audio-visual recording (n = 1), pre-recorded instructions vs. conversational live instructions (n = 1), inclusion of "undress patient" instructions (n = 1), and specific verbal encouragement (n = 1). Studies ranged in methodology from registry studies to randomized clinical trials with the majority being observational studies of simulated EMS calls for OHCA. Outcomes were highly variable but included rates of bystander CPR, confidence & willingness to perform CPR, time to initiation of bystander CPR, bystander CPR quality (including CPR metrics: chest compression depth and rate; chest compression fraction; full chest recoil, ventilation rate, overall CPR competency), rates of automated external defibrillator (AED) use, return of spontaneous circulation (ROSC) and survival. Overall, all interventions seem to be associated with potential improvement in bystander CPR and CPR metrics. Conclusion: There appears to be trends towards improvement on key outcomes however more research is needed. This scoping review highlights the lack of high-quality clinical research on any of the tested interventions to improve DA-CPR. There is insufficient evidence to explore the effectiveness of any of these interventions via systematic review.

An effective no-reference image quality index prediction with a hybrid Artificial Intelligence approach for denoised MRI images.

As the quantity and significance of digital pictures in the medical industry continue to increase, Image Quality Assessment (IQA) has recently become a prevalent subject in the research community. Due to the wide range of distortions that Magnetic Resonance Images (MRI) can experience and the wide variety of information they contain, No-Reference Image Quality Assessment (NR-IQA) has always been a challenging study issue. In an attempt to address this issue, a novel hybrid Artificial Intelligence (AI) is proposed to analyze NR-IQ in massive MRI data. First, the features from the denoised MRI images are extracted using the gray level run length matrix (GLRLM) and EfficientNet B7 algorithm. Next, the Multi-Objective Reptile Search Algorithm (MRSA) was proposed for optimal feature vector selection. Then, the Self-evolving Deep Belief Fuzzy Neural network (SDBFN) algorithm was proposed for the effective NR-IQ analysis. The implementation of this research is executed using MATLAB software. The simulation results are compared with the various conventional methods in terms of correlation coefficient (PLCC), Root Mean Square Error (RMSE), Spearman Rank Order Correlation Coefficient (SROCC) and Kendall Rank Order Correlation Coefficient (KROCC), and Mean Absolute Error (MAE). In addition, our proposed approach yielded a quality number approximately we achieved significant 20% improvement than existing methods, with the PLCC parameter showing a notable increase compared to current techniques. Moreover, the RMSE number decreased by 12% when compared to existing methods. Graphical representations indicated mean MAE values of 0.02 for MRI knee dataset, 0.09 for MRI brain dataset, and 0.098 for MRI breast dataset, showcasing significantly lower MAE values compared to the baseline models.

Taking a health economic perspective in monitoring health inequalities: A focus on excess weight.

BACKGROUND: Traditional approaches to monitoring health inequalities predominantly rely on headcount methods. However, these methods fail to reflect the non-linear health economic implications of changes in disease severity. Alternative, distribution-sensitive metrics are available which could more adequately inform financial planning and policy decision making. METHODS: We describe the design of the Foster-Greer-Thorbecke (FGT) index, and discuss its relative merits as a summary monitoring metric of health inequalities in the population, compared to the Erreygers concentration index. We illustrate the FGT index by conducting a comparative longitudinal analysis of adult excess inequalities in England using Health Survey for England data from 2009 to 2019. FINDINGS: Excess weight inequalities have steadily increased in the English adult population, especially over the last five years. Going beyond headcount, the FGT index analyses revealed that, unlike the rest of the population, the average overweight adult from the most socio-economically deprived group is either obese (30.3 BMI for females) or at the brink of obesity (29.1 BMI for males). These results underscore a deepening divide in obesity severity between communities, with the most socioeconomically deprived groups being increasingly and disproportionally affected. CONCLUSIONS: The FGT index can address some shortcomings of traditional approaches to inequality measurement and local governments should consider adopting it as an alternative population health metric. Future research should apply and develop more refined distribution-sensitive measures of health inequality.

Integrating Sexual and Reproductive Health Equity Into Public Health Goals and Metrics: Comparative Analysis of Healthy People 2030's Approach and a Person-Centered Approach to Contraceptive Access Using Population-Based Data.

BACKGROUND: The Healthy People initiative is a national effort to lay out public health goals in the United States every decade. In its latest iteration, Healthy People 2030, key goals related to contraception focus on increasing the use of effective birth control (contraceptive methods classified as most or moderately effective for pregnancy prevention) among women at risk of unintended pregnancy. This narrow focus is misaligned with sexual and reproductive health equity, which recognizes that individuals' self-defined contraceptive needs are critical for monitoring contraceptive access and designing policy and programmatic strategies to increase access. OBJECTIVE: We aimed to compare 2 population-level metrics of contraceptive access: a conventional metric, use of contraceptive methods considered most or moderately effective for pregnancy prevention among those considered at risk of unintended pregnancy (approximating the Healthy People 2030 approach), and a person-centered metric, use of preferred contraceptive method among current and prospective contraceptive users. METHODS: We used nationally representative data collected in 2022 to construct the 2 metrics of contraceptive access; the overall sample included individuals assigned female at birth not using female sterilization or otherwise infecund and who were not pregnant or trying to become pregnant (unweighted N=2760; population estimate: 43.9 million). We conducted a comparative analysis to examine the convergence and divergence of the metrics by examining whether individuals met the inclusion criteria for the denominators of both metrics, neither metric, only the conventional metric, or only the person-centered metric. RESULTS: Comparing the 2 approaches to measuring contraceptive access, we found that 79% of respondents were either included in or excluded from both metrics (reflecting that the metrics converged when individuals were treated the same by both). The remaining 21% represented divergence in the metrics, with an estimated 5.7 million individuals who did not want to use contraception included only in the conventional metric denominator and an estimated 3.5 million individuals who were using or wanted to use contraception but had never had penile-vaginal sex included only in the person-centered metric denominator. Among those included only in the conventional metric, 100% were content nonusers-individuals who were not using contraception, nor did they want to. Among those included only in the person-centered metric, 68% were currently using contraception. Despite their current or desired contraceptive use, these individuals were excluded from the conventional metric because they had never had penile-vaginal sex. CONCLUSIONS: Our analysis highlights that a frequently used metric of contraceptive access misses the needs of millions of people by simultaneously including content nonusers and excluding those who are using or want to use contraception who have never had sex. Documenting and quantifying the gap between current approaches to assessing contraceptive access and more person-centered ones helps clearly identify where programmatic and policy efforts should focus going forward.

Riparian Landscape Change: A Spatial Approach for Quantifying Change and Development of a River Network Restoration Model.

Natural river landscapes can be biodiversity hotspots but are one of the most human altered ecosystems with habitats significantly damaged around the world, and a third of fish populations threatened with extinction. While riparian ecosystems have been negatively altered by anthropogenic activities, effective planning and restoration strategies can reverse negative impacts by improving habitat quality. However, restoring rivers requires appropriate data on current riparian health while also considering priorities for different stakeholders. To address this, a Geographic Information System (GIS) was used to create a new and transferable restoration priority model based on a section of the river Linth in Switzerland as a case study. The restoration priority model is founded on connectivity, river condition, national priority species and species hotspots. Landscape change of the riparian zone was analyzed using aerial imagery and landscape metrics. Almost a quarter of rivers within the study area were considered high or very high restoration priority, with many aquatic species set to benefit from restoration. From 1946 to 2019, the riparian landscape became highly fragmented due to significant growth in impervious surfaces and a concomitant loss of agricultural land. The GIS model provides a tool by which environmental agencies can manage natural features over large scales, while also planning priorities and targeting conservation strategies to the areas of greatest need.

Lymph node metrics following neoadjuvant therapy to refine patient selection for adjuvant chemotherapy in resected pancreatic cancer: A multi-institutional analysis.

BACKGROUND: In patients with localized pancreatic ductal adenocarcinoma (PDAC) undergoing neoadjuvant therapy (NAT) and resection, selection of adjuvant chemotherapy (AC) is typically guided by high-risk features on histopathologic examination. We evaluated the interaction between post-NAT lymph node metrics and AC receipt on survival. METHODS: Patients who received NAT followed by pancreatectomy (2010-2020) at seven centers were reviewed. Overall survival (OS) in patients receiving AC or not was stratified by lymph node positivity (LNP) or lymph node ratio (LNR) dichotomized at 0.1. Cox models evaluated the independent association between these nodal metrics, AC receipt, and OS. RESULTS: Of 464 patients undergoing NAT and resection, 264 (57%) received AC. Patients selected for AC were younger (median 63 vs. 67 years; p < 0.001), received shorter duration of NAT (2.8 vs. 3.2 months; p = 0.01), had fewer postoperative complications (Clavien-Dindo grade > 3: 1.2% vs. 11.7%; p < 0.001), and lower rates of pathologic complete response (4% vs. 11%; p = 0.01). The median number of nodes evaluated was similar between cohorts (n = 20 in both; p = 0.9). Post-NAT LNP rates were not different, and median LNR was 0.1, in AC and non-AC cohorts. Both LNP (hazard ratio [HR]: 2.1, p < 0.001) and LNR (0 < LNR ≤ 0.1: HR: 1.98, p = 0.002; LNR > 0.1: HR 2.46, p < 0.001) were independently associated with OS on Cox modeling, although receipt of AC was not associated with improved OS (median 30.6 vs. 29.4 months; p = 0.2). In patients with LNR > 0.1, receipt of AC was associated with significantly longer OS compared to non-AC (24 vs. 20 months, respectively; p = 0.04). CONCLUSIONS: LNR following NAT, not simply nodal positivity, may be useful to refine selection of AC in resected PDAC.

Longitudinal bibliometric investigation of published orthodontic research 2013-23: are we investigating patient-reported outcomes?

BACKGROUND: The published literature represents the fundamental basis of any academic specialty, including orthodontics. Orthodontic research outputs provide useful insight into clinical and research priorities, which can help inform future research efforts and resource outputs. In recent years, the need for more patient-reported outcomes in orthodontic research has been highlighted. OBJECTIVES: To identify the most common reported research subjects in orthodontics between 2013-23; (2) identify the main outcomes and types of study design associated with this research, including study design related to patient-reported outcomes; and (3) identify trends in this research activity based upon these findings. MATERIAL AND METHODS: A literature search was performed in a single electronic database (Scopus) to return all indexed publications with relevance to orthodontics published from 2013 to 2023. The 50 most-cited publications per year were then identified. Publication characteristics were extracted using a data collection sheet. Descriptive statistics including frequency distributions were calculated. RESULTS: A total of 14 397 publications were identified. Publications on orthodontic bonding made up 7.02% of all output, followed by materials (5.88%) and tooth movement (5.42%). Subsequent analysis of the most-cited publications per year revealed the most frequently published subjects were aligners (12.5%), orthodontic tooth movement (9.45%), and digital workflow (9.09%), and the most common study designs were in vitro (19.09%) and retrospective observational studies (15.45%). The most common outcome type was morphological features of malocclusion (26.9%). Conversely, patient-focused measures were only reported in 12.7% of studies. CONCLUSIONS: Orthodontic research outputs are dynamic but do show consistent research interest in certain subjects. There is a predilection for the reporting of clinician-focused outcomes; whilst these have some value, more efforts should be focused on conducting rigorous and robust studies that include patient-reported outcomes.

Association between adherence to life's simple 7 metrics and risk of obstructive sleep apnea among adults in the United States.

BACKGROUND: We aimed to explore the impact of adherence to Life's Simple 7 (LS7) metrics on risk of obstructive sleep apnea (OSA), and the impact of inflammation on the association, in adults in the United States. METHODS: Data from 13,825 community-dwelling adults aged ≥ 20 years recruited in the National Health and Nutrition Examination Surveys (NHANES) 2005-2008, 2015-2018 was analyzed. The LS7 score was calculated based on the AHA definition of LS7 metrics. The diagnosis of OSA was based on self-reported symptoms of sleep disturbance using a standard questionnaire. The Multivariable Apnea Prediction (MAP) Index score was also calculated to assess the risk of OSA. Log-binominal regression and negative binomial regression were performed to estimate the associations between LS7 and OSA and MAP index, with odds ratios (ORs) and prevalence ratios (PRs) and their 95% confidence intervals (CIs) calculated. Mediation analysis was performed to estimate the mediating effects of inflammatory indicators on the associations. RESULTS: A total of 4473 participants (32.4%) had OSA, and the mean MAP index was 0.39. In fully adjusted log-binominal regression models, with total score < 6 as the reference, the ORs (95% CIs) for risk of OSA were 0.90 (0.73, 1.10), 0.76 (0.65, 0.89), 0.78 (0.64, 0.95), and 0.45 (0.38, 0.54) for total score = 6, total score = 7, total score = 8, and total score > 8, respectively (P for trend < 0.001). When LS7 score was analyzed as a continuous variable, each 1-point increase in LS7 score was associated with a 15% decrease in OSA risk (P < 0.001). In negative binominal regression models, the adjusted PRs (95% CIs) for the MAP index were 0.93 (0.90, 0.97), 0.87 (0.84, 0.91), 0.80 (0.77, 0.84), and 0.55 (0.53, 0.57) for total score = 6, total score = 7, total score = 8, and total score > 8, respectively (P for trend < 0.001). For each 1-point increase in LS7 score, the risk of OSA decreased by 13% (P < 0.001). Consistent results were observed in subgroup analysis. Mediation analysis indicated that inflammatory factors, including blood cell count, neutrophil count, and C-reactive protein, positively mediated the association of LS7 with OSA, with a mediation proportion of 0.022 (P = 0.04), 0.02 (P = 0.04), and 0.02 (P = 0.02), respectively. CONCLUSIONS: In a nationally representative sample of US adults, adherence to LS7 metrics was independently associated with reduced OSA risk. Inflammation plays a mediating role in the association between LS7 and OSA.

Forecasting model for qualitative prediction of the results of patient-specific quality assurance based on planning and complexity metrics and their interrelations. Pilot study.

Background: The purpose was to analyse the interrelations between planning and complexity metrics and gamma passing rates (GPRs) obtained from VMAT treatments and build the forecasting models for qualitative prediction (QD) of GPRs results. Materials and method: 802 treatment arcs from the plans prepared for the head and neck, thorax, abdomen, and pelvic cancers were analysed. The plans were verified by portal dosimetry and analysed twice using the gamma method with 3%|2mm and 2%|2mm acceptance criteria. The tolerance limit of GPR was 95%. Red, yellow, and green QDs were established for GPR examination. The interrelations were examined, as well as the analysis of effective differentiation of QD. Three models for QD forecasting based on discriminant analysis (DA), random decision forest (RDF) methods, and the hybrid model (HM) were built and evaluated. Results: Most of the interrelations were small or moderate. The exception is correlations of the join function with the average number of monitor units per control point (R = 0.893) and the beam aperture with planning target volume (R = 0.897). While many metrics allow for the effective separation of the QDs from each other, the study shows that predicting the values of the QD is possible only through multi-component forecasting models, of which the HM is the most accurate (0.894). Conclusion: Of the three models explored in this study, the HM, which uses DA methods to predict red QD and RDF methods to predict green and yellow QDs, is the most promising one.

Matters We Metric Vs. Metrics that Matter.

Introduction: Geriatric fracture is a pressing global health issue, marked by elevated mortality and morbidity rates and escalating health care costs. The evolving health care system from fee-for-service to quality-based reimbursement has led to externally driven reward and reimbursement systems that may not account for the complexity of caring for older adults with fracture. Significance: The aim of this review is to highlight the need for a shift towards meaningful metrics that impact geriatric fracture care and to issue a call to action for all medical societies to advocate for national reimbursement and ranking systems that focus on metrics that truly matter. Results: Traditional metrics, while easier to capture, may not necessarily represent high quality care and may even have unintentional adverse consequences. For example, the focus on reducing length of stay may lead to older patients being discharged too early, without adequately addressing pain, constipation, or delirium. In addition, a focus on mortality may miss the opportunity to deliver compassionate end-of-life care. Existing geriatric fracture care metrics have expanded beyond traditional metrics to include assessment by geriatricians, fracture prevention, and delirium assessments. However, there is a need to further consider and develop patient-focused metrics. The Age-Friendly Health Initiative (4 Ms), which includes Mobility, Medication, Mentation, and what Matters is an evidence-based framework for assessing and acting on critical issues in the care of older adults. Additional metrics that should be considered include an assessment of nutrition and secondary fracture prevention. Conclusion: In the realm of geriatric fracture care, the metrics currently employed often revolve around adherence to established guidelines and are heavily influenced by financial considerations. It is crucial to shift the paradigm towards metrics that truly matter for geriatric fracture patients, recognizing the multifaceted nature of their care and the profound impact these fractures have on their lives.

Technical note: A software tool to extract complexity metrics from radiotherapy treatment plans.

BACKGROUND: Complexity metrics are mathematical quantities designed to quantify aspects of radiotherapy treatment plans that may affect both their deliverability and dosimetric accuracy. Despite numerous studies investigating their utility, there remains a notable absence of shared tools for their extraction. PURPOSE: This study introduces UCoMX (Universal Complexity Metrics Extractor), a software package designed for the extraction of complexity metrics from the DICOM-RT plan files of radiotherapy treatments. METHODS: UCoMX is developed around two extraction engines: VCoMX (VMAT Complexity Metrics Extractor) for VMAT/IMRT plans, and TCoMX (Tomotherapy Complexity Metrics Extractor) tailored for Helical Tomotherapy plans. The software, built using Matlab, is freely available in both Matlab-based and stand-alone versions. More than 90 complexity metrics, drawn from relevant literature, are implemented in the package: 43 for VMAT/IMRT and 51 for Helical Tomotherapy. RESULTS: The package is designed to read DICOM-RT plan files generated by most commercially available Treatment Planning Systems (TPSs), across various treatment units. A reference dataset containing VMAT, IMRT, and Helical Tomotherapy plans is provided to serve as a reference for comparing UCoMX with other in-house systems available at other centers. CONCLUSION: UCoMX offers a straightforward solution for extracting complexity metrics from radiotherapy plans. Its versatility is enhanced through different versions, including Matlab-based and stand-alone, and its compatibility with a wide range of commercially available TPSs and treatment units. UCoMX presents a free, user-friendly tool empowering researchers to compute the complexity of treatment plans efficiently.

SIMpat: A Synthetic Benchmark for Similarity Metrics on Patient Representations.

Similarity and clustering tasks based on data extracted from electronic health records on the patient level suffer from the curse of dimensionality and the lack of inter-patient data comparability. Indeed, for many health institutions, there are many more variables, and ways of expressing those variables to represent patients than patients sharing the same set of data. To lower redundancy and increase interoperability one strategy is to map data to semantic-driven representations through medical knowledge graphs such as SNOMED-CT. However, patient similarity metrics based on this knowledge-graph information lack quantitative evaluation and comparisons with pure data-driven methods. The reasons are twofold, firstly, it is hard to conceptually assess and formalize a gold-standard similarity between patients resulting in poor inter-annotator agreement in qualitative evaluations. Secondly, the community has been lacking a clear benchmark to compare existing metrics developed by scientific communities coming from various fields such as ontology, data science, and medical informatics. This study proposes to leverage the known challenges of evaluating patient similarities by proposing SIMpat, a synthetic benchmark to quantitatively evaluate available metrics, based on controlled cohorts, which could later be used to assess their sensibility regarding aspects such as the sparsity of variables or specificities of patient disease patterns.

Multi-metric comparison of machine learning imputation methods with application to breast cancer survival.

Handling missing data in clinical prognostic studies is an essential yet challenging task. This study aimed to provide a comprehensive assessment of the effectiveness and reliability of different machine learning (ML) imputation methods across various analytical perspectives. Specifically, it focused on three distinct classes of performance metrics used to evaluate ML imputation methods: post-imputation bias of regression estimates, post-imputation predictive accuracy, and substantive model-free metrics. As an illustration, we applied data from a real-world breast cancer survival study. This comprehensive approach aimed to provide a thorough assessment of the effectiveness and reliability of ML imputation methods across various analytical perspectives. A simulated dataset with 30% Missing At Random (MAR) values was used. A number of single imputation (SI) methods - specifically KNN, missMDA, CART, missForest, missRanger, missCforest - and multiple imputation (MI) methods - specifically miceCART and miceRF - were evaluated. The performance metrics used were Gower's distance, estimation bias, empirical standard error, coverage rate, length of confidence interval, predictive accuracy, proportion of falsely classified (PFC), normalized root mean squared error (NRMSE), AUC, and C-index scores. The analysis revealed that in terms of Gower's distance, CART and missForest were the most accurate, while missMDA and CART excelled for binary covariates; missForest and miceCART were superior for continuous covariates. When assessing bias and accuracy in regression estimates, miceCART and miceRF exhibited the least bias. Overall, the various imputation methods demonstrated greater efficiency than complete-case analysis (CCA), with MICE methods providing optimal confidence interval coverage. In terms of predictive accuracy for Cox models, missMDA and missForest had superior AUC and C-index scores. Despite offering better predictive accuracy, the study found that SI methods introduced more bias into the regression coefficients compared to MI methods. This study underlines the importance of selecting appropriate imputation methods based on study goals and data types in time-to-event research. The varying effectiveness of methods across the different performance metrics studied highlights the value of using advanced machine learning algorithms within a multiple imputation framework to enhance research integrity and the robustness of findings.

Reliability of quantitative magnetic susceptibility imaging metrics for cerebral cortex and major subcortical structures.

BACKGROUND AND PURPOSE: Susceptibility estimates derived from quantitative susceptibility mapping (QSM) images for the cerebral cortex and major subcortical structures are variably reported in brain magnetic resonance imaging (MRI) studies, as average of all ( µ all ${{{{\mu}}}_{{\mathrm{all}}}}$ ), absolute ( µ abs ${{{{\mu}}}_{{\mathrm{abs}}}}$ ), or positive- ( µ p ${{{{\mu}}}_{\mathrm{p}}}$ ) and negative-only ( µ n ${{{{\mu}}}_{\mathrm{n}}}$ ) susceptibility values using a region of interest (ROI) approach. This pilot study presents a reliability analysis of currently used ROI-QSM metrics and an alternative ROI-based approach to obtain voxel-weighted ROI-QSM metrics ( µ wp ${{{{\mu}}}_{{\mathrm{wp}}}}$ and µ wn ${{{{\mu}}}_{{\mathrm{wn}}}}$ ). METHODS: Ten healthy subjects underwent repeated (test-retest) 3-dimensional multi-echo gradient-echo (3DMEGE) 3 Tesla MRI measurements. Complex-valued 3DMEGE images were acquired and reconstructed with slice thicknesses of 1 and 2 mm (3DMEGE1, 3DMEGE2) along with 3DT1-weighted isometric (voxel 1 mm3) images for independent registration and ROI segmentation. Agreement, consistency, and reproducibility of ROI-QSM metrics were assessed through Bland-Altman analysis, intraclass correlation coefficient, and interscan and intersubject coefficient of variation (CoV). RESULTS: All ROI-QSM metrics exhibited good to excellent consistency and test-retest agreement with no proportional bias. Interscan CoV was higher for µ all ${{{{\mu}}}_{{\mathrm{all}}}}$ in comparison to the other metrics where it was below 15%, in both 3DMEGE1 and 3DMEGE2 datasets. Intersubject CoV for µ all ${{{{\mu}}}_{{\mathrm{all}}}}$ and µ abs ${{{{\mu}}}_{{\mathrm{abs}}}}$ exceeded 50% in all ROIs. CONCLUSIONS: Among the evaluated ROI-QSM metrics, µ all ${{{{\mu}}}_{{\mathrm{all}}}}$ and µ abs ${{{{\mu}}}_{{\mathrm{abs}}}}$ estimates were less reliable, whereas separating positive and negative values (using µ p , µ n , µ wp , µ wn ${{{{\mu}}}_{\mathrm{p}}},\ {{{{\mu}}}_{\mathrm{n}}},\ {{{{\mu}}}_{{\mathrm{wp}}}},\ {{{{\mu}}}_{{\mathrm{wn}}}}$ ) improved the reproducibility within, and the comparability between, subjects, even when reducing the slice thickness. These preliminary findings may offer valuable insights toward standardizing ROI-QSM metrics across different patient cohorts and imaging settings in future clinical MRI studies.

The relationship between off-ice testing and on-ice performance in male youth Ice hockey players.

Introduction: Ice hockey demands a unique blend of physical fitness and skill, necessitating a comprehensive understanding of the factors influencing on-ice performance. The present study was designed to examine the relationship between off-ice and on-ice performance measures in male, youth, ice hockey players. Methods: Eleven minor hockey players (Age = 9.8 ± 1.1 years) participated in two testing days: (1) off-ice and (2) on-ice assessments. Off-ice assessments included maximal aerobic power, anaerobic fitness, muscular strength (handgrip and single leg squat), muscular endurance (curl-ups and push-ups), muscular power (standing long jump and vertical jump), and 30 m sprinting speed and acceleration. On-ice testing included a 15.2 m maximum speed test, a 6.1 m acceleration test with a continuation into a 47.9 m top speed test, an agility cornering S turn test, and a shot velocity test. Results: Twenty-four out of 33 off-ice variables were significantly correlated with at least one of the 11 on-ice performance variables. From those 24, 10 were included as predictors for at least one of the on-ice performance variables. Each model was composed of either one or two predictors, where the most common predictors were 30 m Run - Split (6.1 m) Sprint time and 30 m Run - Total (30 m) Sprint Max speed (included in four out of 11 models each). The prediction formulas R2 and coefficient of variation ranged from 0.63% to 0.96% and 1.2% to 15.3%, respectively. Discussion: Diverse off-ice measures of aerobic fitness, anaerobic power, muscular strength, power, and endurance, and sprinting speed, acceleration, and agility are predictive of on-ice performance. The insights gained from this study contribute to the refinement of assessment protocols, fostering a comprehensive approach to optimizing player performance and potential. Understanding the connection between objective off-ice testing and on-ice performance can support tailored training programs and player development in male youth ice hockey.

Which orthodontic articles are accessed online the most? Exploring article usage metrics along with citations and altmetrics.

OBJECTIVES: To investigate the current state of article usage metrics in orthodontics. MATERIALS AND METHODS: Out of all orthodontic journals listed in Journal Citation Reports 2022, the European Journal of Orthodontics and The Angle Orthodontist fulfilled the inclusion criteria. All journal issues published in 2021 were scrutinized for original research articles and systematic reviews/meta-analyses, and the following features were collected: article type, subject, title, number of words and authors, Altmetric Attention Score (AAS), X (formerly Twitter) posts, and Mendeley reads. Article citations, number of publications, and h-index of the last authors were retrieved from Web of Science (WoS). RESULTS: 181 articles were considered eligible. The median number of views and downloads of included articles was 1296 (range: 355-10,233) and 793 (range: 167-3629). Page views, downloads, and total views were significantly correlated with WoS citations (rho > 0.345; P < .001). There was no correlation between usage metrics, AAS, X posts, and Mendeley reads. Number of downloads were significantly higher in studies dealing with new technologies, and where the last author had 1-40 publications or an h-index of 0-30. CONCLUSIONS: Page views, downloads, and total views were positively correlated with WoS citations and, therefore, may serve as an early estimate of future citations. Significant variations in article downloads may be expected in relation to article subject, scientific productivity, and impact of the last authors.

Unexpected changes in source apportioned results derived from different ambient VOC metrics.

Although most source apportionments of VOCs use mixing ratios, about 23 % of published studies use mass concentrations. Thus, systematically exploring the changes in VOC source apportioned results caused by metric differences is important to assess the differences in key precursor apportionment results given the observed increases in O3 pollution situation. Different monitoring instruments measured hourly VOC volumetric concentrations in three typical Chinese cities (i.e., Qingdao, Shijiazhuang, and Zhumadian). Converting volumetric to mass concentrations under standard and/or actual temperature-pressure conditions, VOC values with different metrics were obtained. The impacts of different metrics on the source apportionments were then investigated. Compared to the positive matrix factorization of the volumetric data (VC-PMF), the VOC species concentrations with low relative molecular mass (RMM) in the factor profiles substantially decreased in mass data analyses (MC-PMF). However, those species with high RMM substantially increased. There were no substantial differences in the apportioned source contributions based on standard and actual condition mass concentrations. However, the high-low rankings of percent contributions apportioned using the volumetric and mass data produced substantial differences. Compared with the VC-PMF results, the percent contributions of sources dominated by species with low RMM (e.g., natural gas usage and mixed sources containing natural gas usage) apportioned by MC-PMF decreased, while those of sources that emitted high RMM species (e.g., solvent usage and mixed sources containing solvent usage) increased. Source apportionments based on the volumetric concentration data had more practical significance compared to the mass concentration data results for control strategy development since the mass data analyses created issues.