Does engagement matter? The impact of patient and community engagement on implementation of cardiovascular health materials in primary care settings.

BACKGROUND: Engaging patients and community members in healthcare implementation, research and evaluation has become more popular over the past two decades. Despite the growing interest in patient engagement, there is scant evidence of its impact and importance. Boot Camp Translation (BCT) is one evidence-based method of engaging communities in research. The purpose of this report is to describe the uptake by primary care practices of cardiovascular disease prevention materials produced through four different local community engagement efforts using BCT. METHODS: EvidenceNOW Southwest (ENSW) was a randomized trial to increase cardiovascular disease (CVD) prevention in primary care practices. Because of its study design, Four BCTs were conducted, and the materials created were made available to participating practices in the "enhanced" study arm. As a result, ENSW offered one of the first opportunities to explore the impact of the BCT method by describing the uptake by primary care practices of health messages and materials created locally using the BCT process. Analysis compared uptake of locally translated BCT products vs. all other products among practices based on geography, type of practice, and local BCT. RESULTS: Within the enhanced arm of the study that included BCT, 69 urban and 13 rural practices participated with 9 being federally qualified community health centers, 14 hospital owned and 59 clinician owned. Sixty-three practices had 5 or fewer clinicians. Two hundred and ten separate orders for materials were placed by 43 of the 82 practices. While practices ordered a wide variety of BCT products, they were more likely to order materials developed by their local BCT. CONCLUSIONS: In this study, patients and community members generated common and unique messages and materials for cardiovascular disease prevention relevant to their regional and community culture. Primary care practices preferred the materials created in their region. The greater uptake of locally created materials over non-local materials supports the use of patient engagement methods such as BCT to increase the implementation and delivery of guideline-based care. Yes, patient and community engagement matters. TRIAL REGISTRATION AND IRB: Trial registration was prospectively registered on July 31, 2015 at ClinicalTrials.gov (NCT02515578, protocol identifier 15-0403). The project was approved by the Colorado Multiple Institutional Review Board and the University of New Mexico Human Research Protections Office.

Modelling count data with partial differential equation models in biology.

Partial differential equation (PDE) models are often used to study biological phenomena involving movement-birth-death processes, including ecological population dynamics and the invasion of populations of biological cells. Count data, by definition, is non-negative, and count data relating to biological populations is often bounded above by some carrying capacity that arises through biological competition for space or nutrients. Parameter estimation, parameter identifiability, and making model predictions usually involves working with a measurement error model that explicitly relating experimental measurements with the solution of a mathematical model. In many biological applications, a typical approach is to assume the data are normally distributed about the solution of the mathematical model. Despite the widespread use of the standard additive Gaussian measurement error model, the assumptions inherent in this approach are rarely explicitly considered or compared with other options. Here, we interpret scratch assay data, involving migration, proliferation and delays in a population of cancer cells using a reaction-diffusion PDE model. We consider relating experimental measurements to the PDE solution using a standard additive Gaussian measurement error model alongside a comparison to a more biologically realistic binomial measurement error model. While estimates of model parameters are relatively insensitive to the choice of measurement error model, model predictions for data realisations are very sensitive. The standard additive Gaussian measurement error model leads to biologically inconsistent predictions, such as negative counts and counts that exceed the carrying capacity across a relatively large spatial region within the experiment. Furthermore, the standard additive Gaussian measurement error model requires estimating an additional parameter compared to the binomial measurement error model. In contrast, the binomial measurement error model leads to biologically plausible predictions and is simpler to implement. We provide open source Julia software on GitHub to replicate all calculations in this work, and we explain how to generalise our approach to deal with coupled PDE models with several dependent variables through a multinomial measurement error model, as well as pointing out other potential generalisations by linking our work with established practices in the field of generalised linear models.

How osteons form: A quantitative hypothesis-testing analysis of cortical pore filling and wall asymmetry.

Osteon morphology provides valuable information about the interplay between different processes involved in bone remodelling. The correct quantitative interpretation of these morphological features is challenging due to the complexity of interactions between osteoblast behaviour, and the evolving geometry of cortical pores during pore closing. We present a combined experimental and mathematical modelling study to provide insights into bone formation mechanisms during cortical bone remodelling based on histological cross-sections of quiescent human osteons and hypothesis-testing analyses. We introduce wall thickness asymmetry as a measure of the local asymmetry of bone formation within an osteon and examine the frequency distribution of wall thickness asymmetry in cortical osteons from human iliac crest bone samples from women 16-78 years old. Our measurements show that most osteons possess some degree of asymmetry, and that the average degree of osteon asymmetry in cortical bone evolves with age. We then propose a comprehensive mathematical model of cortical pore filling that includes osteoblast secretory activity, osteoblast elimination, osteoblast embedment as osteocytes, and osteoblast crowding and redistribution along the bone surface. The mathematical model is first calibrated to symmetric osteon data, and then used to test three mechanisms of asymmetric wall formation against osteon data: (i) delays in the onset of infilling around the cement line; (ii) heterogeneous osteoblastogenesis around the bone perimeter; and (iii) heterogeneous osteoblast secretory rate around the bone perimeter. Our results suggest that wall thickness asymmetry due to off-centred Haversian pores within osteons, and that nonuniform lamellar thicknesses within osteons are important morphological features that can indicate the prevalence of specific asymmetry-generating mechanisms. This has significant implications for the study of disruptions of bone formation as it could indicate what biological bone formation processes may become disrupted with age or disease.

Implementing measurement error models with mechanistic mathematical models in a likelihood-based framework for estimation, identifiability analysis and prediction in the life sciences.

Throughout the life sciences, we routinely seek to interpret measurements and observations using parametrized mechanistic mathematical models. A fundamental and often overlooked choice in this approach involves relating the solution of a mathematical model with noisy and incomplete measurement data. This is often achieved by assuming that the data are noisy measurements of the solution of a deterministic mathematical model, and that measurement errors are additive and normally distributed. While this assumption of additive Gaussian noise is extremely common and simple to implement and interpret, it is often unjustified and can lead to poor parameter estimates and non-physical predictions. One way to overcome this challenge is to implement a different measurement error model. In this review, we demonstrate how to implement a range of measurement error models in a likelihood-based framework for estimation, identifiability analysis and prediction, called profile-wise analysis. This frequentist approach to uncertainty quantification for mechanistic models leverages the profile likelihood for targeting parameters and understanding their influence on predictions. Case studies, motivated by simple caricature models routinely used in systems biology and mathematical biology literature, illustrate how the same ideas apply to different types of mathematical models. Open-source Julia code to reproduce results is available on GitHub.

Formation and Growth of Co-Culture Tumour Spheroids: New Compartment-Based Mathematical Models and Experiments.

Co-culture tumour spheroid experiments are routinely performed to investigate cancer progression and test anti-cancer therapies. Therefore, methods to quantitatively characterise and interpret co-culture spheroid growth are of great interest. However, co-culture spheroid growth is complex. Multiple biological processes occur on overlapping timescales and different cell types within the spheroid may have different characteristics, such as differing proliferation rates or responses to nutrient availability. At present there is no standard, widely-accepted mathematical model of such complex spatio-temporal growth processes. Typical approaches to analyse these experiments focus on the late-time temporal evolution of spheroid size and overlook early-time spheroid formation, spheroid structure and geometry. Here, using a range of ordinary differential equation-based mathematical models and parameter estimation, we interpret new co-culture experimental data. We provide new biological insights about spheroid formation, growth, and structure. As part of this analysis we connect Greenspan's seminal mathematical model to co-culture data for the first time. Furthermore, we generalise a class of compartment-based spheroid mathematical models that have previously been restricted to one population so they can be applied to multiple populations. As special cases of the general model, we explore multiple natural two population extensions to Greenspan's seminal model and reveal biological mechanisms that can describe the internal dynamics of growing co-culture spheroids and those that cannot. This mathematical and statistical modelling-based framework is well-suited to analyse spheroids grown with multiple different cell types and the new class of mathematical models provide opportunities for further mathematical and biological insights.

Profile-Wise Analysis: A profile likelihood-based workflow for identifiability analysis, estimation, and prediction with mechanistic mathematical models.

Interpreting data using mechanistic mathematical models provides a foundation for discovery and decision-making in all areas of science and engineering. Developing mechanistic insight by combining mathematical models and experimental data is especially critical in mathematical biology as new data and new types of data are collected and reported. Key steps in using mechanistic mathematical models to interpret data include: (i) identifiability analysis; (ii) parameter estimation; and (iii) model prediction. Here we present a systematic, computationally-efficient workflow we call Profile-Wise Analysis (PWA) that addresses all three steps in a unified way. Recently-developed methods for constructing 'profile-wise' prediction intervals enable this workflow and provide the central linkage between different workflow components. These methods propagate profile-likelihood-based confidence sets for model parameters to predictions in a way that isolates how different parameter combinations affect model predictions. We show how to extend these profile-wise prediction intervals to two-dimensional interest parameters. We then demonstrate how to combine profile-wise prediction confidence sets to give an overall prediction confidence set that approximates the full likelihood-based prediction confidence set well. Our three case studies illustrate practical aspects of the workflow, focusing on ordinary differential equation (ODE) mechanistic models with both Gaussian and non-Gaussian noise models. While the case studies focus on ODE-based models, the workflow applies to other classes of mathematical models, including partial differential equations and simulation-based stochastic models. Open-source software on GitHub can be used to replicate the case studies.

Association Between Antibiotic Prescribing and Time to Diagnosis of Head and Neck Cancer.

Importance: Diagnostic delay can negatively affect patient outcomes in head and neck cancer (HNC). Neck mass and other symptoms of undiagnosed HNC may be treated with antibiotics, delaying diagnosis and treatment, despite current clinical practice guidelines. Objective: To investigate temporal trends, associated factors, and time from symptom onset to antibiotic prescribing before an HNC diagnosis. Design, Setting, and Participants: A retrospective cohort study was conducted using data obtained from a deidentified electronic health records data set from January 1, 2011, to December 31, 2018. Patients with HNC enrolled in the data set for at least 1 year before diagnosis date determined by either 1 inpatient encounter or first of 2 outpatient encounters within 6 months were included. Data analysis was conducted from May 1 to November 9, 2022. Exposure: Antibiotic prescription within 3 months before HNC diagnosis date. Main Outcomes and Measures: The primary outcome was days from the first documented symptom to HNC diagnosis. Results: The cohort included 7811 patients with HNC (4151 [53.1%] men, mean [SD] age, 60.2 [15.8] years). At least 1 antibiotic was prescribed for 1219 patients (15.6%) within 3 months before HNC diagnosis. This represented an increase over the 8.9% prescribing rate during the baseline period 12 to 9 months before diagnosis. The rate of antibiotic prescribing within 3 months before diagnosis did not change significantly over time (quarterly percent change, 0.49%; 95% CI, -3.06% to 4.16%). Patients receiving an antibiotic prescription within 3 months of an HNC diagnosis had a 21.1% longer time between symptom onset and HNC diagnoses (adjusted rate ratio [ARR], 1.21; 95% CI, 1.14-1.29). Compared with diagnosis by otolaryngologists, primary care/internal medicine physicians were most likely to prescribe antibiotics for patients who were diagnosed with a presenting symptom (adjusted prevalence ratio, 1.60; 95% CI, 1.27-2.02). In patients presenting with neck mass/swelling, those presenting with other symptoms were more likely to have longer intervals from symptom onset to diagnosis (ARR, 1.31; 95% CI, 1.08-1.59). Conclusions and Relevance: The findings of this cohort study suggest there is an increased rate of antibiotic prescription in the 3 months before HNC diagnosis, which is associated with an increased time to diagnosis. These findings identify an area for improvement in HNC care and guidelines.

Approach to sialadenitis.

OBJECTIVE: To provide family physicians with a practical evidence-based approach to the management of patients with sialadenitis. SOURCES OF INFORMATION: MEDLINE and PubMed databases were searched for English-language research on sialadenitis and other salivary gland disorders, as well as for relevant review articles and guidelines published between 1981 and 2021. MAIN MESSAGE: Sialadenitis refers to inflammation or infection of the salivary glands and is a condition that can be caused by a broad range of processes including infectious, obstructive, and autoimmune. History and physical examination play important roles in directing management, while imaging is often useful to establish a diagnosis. Red flags such as suspected abscess formation, signs of respiratory obstruction, facial paresis, and fixation of a mass to underlying tissue should prompt urgent referral to head and neck surgery or a visit to the emergency department. CONCLUSION: Family physicians can play an important role in the diagnosis and management of sialadenitis. Prompt recognition and treatment of the condition can prevent the development of complications.

Approche de la sialadénite.

OBJECTIF: Proposer aux médecins de famille une approche pratique fondée sur des données probantes pour la prise en charge de patients souffrant de sialadénite. SOURCES DE L'INFORMATION: Une recension a été effectuée dans les bases de données MEDLINE et PubMed pour trouver des recherches publiées en anglais sur la sialadénite et d'autres troubles des glandes salivaires, ainsi que des revues et des lignes directrices pertinentes, publiées entre 1981 et 2021. MESSAGE PRINCIPAL: La sialadénite désigne une inflammation ou une infection des glandes salivaires; elle peut être causée par un large éventail de processus de nature infectieuse, obstructive et auto-immune. L'anamnèse et l'examen physique jouent un rôle important pour orienter la prise en charge, tandis que l'imagerie est souvent utile pour établir un diagnostic. Des signaux d'alerte comme la formation suspectée d'un abcès, des signes d'obstruction respiratoire, une parésie faciale et la fixation d'une masse aux tissus sous-jacents devraient inciter à faire une demande de consultation urgente en chirurgie de la tête et du cou, ou à recommander une visite au service d'urgence. CONCLUSION: Les médecins de famille peuvent jouer un rôle important dans le diagnostic et la prise en charge de la sialadénite. Une reconnaissance et un traitement rapides du problème peuvent prévenir la survenance de complications.

The "Medicare effect" on head and neck cancer diagnosis and survival.

BACKGROUND: Uninsured individuals age 55-64 experience disproportionately poor outcomes compared to their insured counterparts. Adequate coverage may prevent these delays. This study investigates a "Medicare-effect" on head and neck squamous cell carcinoma (HNSCC) diagnosis and treatment. METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was queried for persons ages 60-70 years in the United States from 2000 to 2016 with HNSCC. A "Medicare effect" was defined as an increase in incidence, reduction in advanced stage presentation, and/or decrease in cancer-specific mortality (CSM). RESULTS: Compared to their Medicaid or uninsured counterparts, patients age 65 have an increased incidence of HNSCC diagnosis, reduction in advanced stage presentation, decrease in cancer-specific mortality, and higher likelihood of receiving cancer-specific surgery. CONCLUSIONS: Patients age 65 with Medicare have decreased incidence of HNSCC, less hazard of late-stage diagnosis, and lower cancer-specific mortality than their Medicaid or uninsured counterparts, supporting the idea of a "Medicare effect" in HNSCC.

Implementation strategies to address the determinants of adoption, implementation, and maintenance of a clinical decision support tool for emergency department buprenorphine initiation: a qualitative study.

BACKGROUND: Untreated opioid use disorder (OUD) is a significant public health problem. Buprenorphine is an evidence-based treatment for OUD that can be initiated in and prescribed from emergency departments (EDs) and office settings. Adoption of buprenorphine initiation among ED clinicians is low. The EMBED pragmatic clinical trial investigated the effectiveness of a clinical decision support (CDS) tool to promote ED clinicians' behavior related to buprenorphine initiation in the ED. While the CDS intervention was not associated with increased rates of buprenorphine treatment for patients with OUD at intervention ED sites, attending physicians at intervention EDs were more likely to initiate buprenorphine at least once over the duration of the study compared to those in the usual care arms (44.4% vs 34.0%, P = 0.01). This suggests the CDS intervention may be associated with increased adoption of buprenorphine initiation. As a secondary aim, we sought to identify the determinants of CDS adoption, implementation, and maintenance in a variety of ED settings and geographic locations. METHODS: We purposively sampled and conducted semi-structured, in-depth interviews with clinicians across EMBED trial sites randomized to the intervention arm from five healthcare systems. Interviews elicited clinician experiences regarding buprenorphine initiation and CDS use. Interviews were analyzed using directed content analysis informed by the Practical, Robust Implementation and Sustainability Model (PRISM). We used a hybrid approach (a priori codes informed by PRISM and emergent codes) for codebook development. ATLAS.ti (version 9.0) was used for data management. Coded data were analyzed within individual interview transcripts and across all interviews to identify major themes. This process involved (1) combining, comparing, and making connections between codes; (2) writing analytic memos about observed patterns; and (3) frequent team meetings to discuss emerging patterns. RESULTS: Twenty-eight interviews were conducted. Major themes that influenced the successful adoption, implementation, and maintenance of the EMBED intervention and ED-initiated BUP were organizational culture and commitment, clinician training and support, the ability to connect patients to ongoing treatment, and the ability to tailor implementation to each ED. These findings informed the identification of implementation strategies (framed using PRISM domains) to enhance the ED initiation of buprenorphine. CONCLUSION: The findings from this qualitative analysis can provide guidance to build better systems to promote the adoption of ED-initiated buprenorphine.

High-risk human papillomavirus 16/18 associated with improved survival in sinonasal squamous cell carcinoma.

BACKGROUND: There has been conflicting evidence on the independent prognostic role of human papillomavirus (HPV) status in sinonasal cancer. The objective of this study was to assess whether the survival of patients with sinonasal cancer differs based on various HPV statuses, including HPV-negative, positive for the high-risk HPV-16 and HPV-18 (HPV16/18) subtypes, and positive for other high-risk and low-risk HPV subtypes. METHODS: In this retrospective cohort study, data from the National Cancer Database were extracted from the years 2010-2017 for patients who had primary sinonasal cancer (N = 12,009). The outcome of interest was overall survival based on HPV tumor status. RESULTS: Study included an analytic cohort of 1070 patients with sinonasal cancer who had confirmed HPV tumor status (732 [68.4%] HPV-negative; 280 [26.2%] HPV16/18-positive; 40 [3.7%] positive for other high-risk HPV; and 18 [1.7%] positive for low-risk HPV). HPV-negative patients had the lowest all-cause survival probability at 5 years postdiagnosis (0.50). After controlling for covariates, HPV16/18-positive patients had a 37% lower mortality hazard than HPV-negative patients (adjusted hazard ratio, 0.63; 95% confidence interval [CI], 0.48-0.82). Patients aged 64-72 years (crude prevalence ratio, 0.66; 95% CI, 0.51-0.86) and 73 years and older (crude prevalence ratio, 0.43; 95% CI, 0.31-0.59) presented with lower rates of HPV16/18-positive sinonasal cancer than those aged 40-54 years. In addition, Hispanic patients had a 2.36 times higher prevalence of non-HPV16/18 sinonasal cancer than non-Hispanic White patients. CONCLUSIONS: These data suggest that, for patients with sinonasal cancer, HPV16/18-positive disease may confer a significant survival advantage compared with HPV-negative disease. Other high-risk and low-risk HPV subtypes have survival rates similar to the rates for HPV-negative disease. HPV status might be an important independent prognostic factor in sinonasal cancer that could be used in patient selection and clinical decisions.

Geometric analysis enables biological insight from complex non-identifiable models using simple surrogates.

An enduring challenge in computational biology is to balance data quality and quantity with model complexity. Tools such as identifiability analysis and information criterion have been developed to harmonise this juxtaposition, yet cannot always resolve the mismatch between available data and the granularity required in mathematical models to answer important biological questions. Often, it is only simple phenomenological models, such as the logistic and Gompertz growth models, that are identifiable from standard experimental measurements. To draw insights from complex, non-identifiable models that incorporate key biological mechanisms of interest, we study the geometry of a map in parameter space from the complex model to a simple, identifiable, surrogate model. By studying how non-identifiable parameters in the complex model quantitatively relate to identifiable parameters in surrogate, we introduce and exploit a layer of interpretation between the set of non-identifiable parameters and the goodness-of-fit metric or likelihood studied in typical identifiability analysis. We demonstrate our approach by analysing a hierarchy of mathematical models for multicellular tumour spheroid growth experiments. Typical data from tumour spheroid experiments are limited and noisy, and corresponding mathematical models are very often made arbitrarily complex. Our geometric approach is able to predict non-identifiabilities, classify non-identifiable parameter spaces into identifiable parameter combinations that relate to features in the data characterised by parameters in a surrogate model, and overall provide additional biological insight from complex non-identifiable models.

Growth and adaptation mechanisms of tumour spheroids with time-dependent oxygen availability.

Tumours are subject to external environmental variability. However, in vitro tumour spheroid experiments, used to understand cancer progression and develop cancer therapies, have been routinely performed for the past fifty years in constant external environments. Furthermore, spheroids are typically grown in ambient atmospheric oxygen (normoxia), whereas most in vivo tumours exist in hypoxic environments. Therefore, there are clear discrepancies between in vitro and in vivo conditions. We explore these discrepancies by combining tools from experimental biology, mathematical modelling, and statistical uncertainty quantification. Focusing on oxygen variability to develop our framework, we reveal key biological mechanisms governing tumour spheroid growth. Growing spheroids in time-dependent conditions, we identify and quantify novel biological adaptation mechanisms, including unexpected necrotic core removal, and transient reversal of the tumour spheroid growth phases.

Predicting Neoadjuvant Treatment Response in Rectal Cancer Using Machine Learning: Evaluation of MRI-Based Radiomic and Clinical Models.

BACKGROUND: Radiomics is an approach to medical imaging that quantifies the features normally translated into visual display. While both radiomic and clinical markers have shown promise in predicting response to neoadjuvant chemoradiation therapy (nCRT) for rectal cancer, the interrelationship is not yet clear. METHODS: A retrospective, single-institution study of patients treated with nCRT for locally advanced rectal cancer was performed. Clinical and radiomic features were extracted from electronic medical record and pre-treatment magnetic resonance imaging, respectively. Machine learning models were created and assessed for complete response and positive treatment effect using the area under the receiver operating curves. RESULTS: Of 131 rectal cancer patients evaluated, 68 (51.9%) were identified to have a positive treatment effect and 35 (26.7%) had a complete response. On univariate analysis, clinical T-stage (OR 0.46, p = 0.02), lymphovascular/perineural invasion (OR 0.11, p = 0.03), and statin use (OR 2.45, p = 0.049) were associated with a complete response. Clinical T-stage (OR 0.37, p = 0.01), lymphovascular/perineural invasion (OR 0.16, p = 0.001), and abnormal carcinoembryonic antigen level (OR 0.28, p = 0.002) were significantly associated with a positive treatment effect. The clinical model was the strongest individual predictor of both positive treatment effect (AUC = 0.64) and complete response (AUC = 0.69). The predictive ability of a positive treatment effect increased by adding tumor and mesorectal radiomic features to the clinical model (AUC = 0.73). CONCLUSIONS: The use of a combined model with both clinical and radiomic features resulted in the strongest predictive capability. With the eventual goal of tailoring treatment to the individual, both clinical and radiologic markers offer insight into identifying patients likely to respond favorably to nCRT.

Profile likelihood-based parameter and predictive interval analysis guides model choice for ecological population dynamics.

Calibrating mathematical models to describe ecological data provides important insight via parameter estimation that is not possible from analysing data alone. When we undertake a mathematical modelling study of ecological or biological data, we must deal with the trade-off between data availability and model complexity. Dealing with the nexus between data availability and model complexity is an ongoing challenge in mathematical modelling, particularly in mathematical biology and mathematical ecology where data collection is often not standardised, and more broad questions about model selection remain relatively open. Therefore, choosing an appropriate model almost always requires case-by-case consideration. In this work we present a straightforward approach to quantitatively explore this trade-off using a case study exploring mathematical models of coral reef regrowth after some ecological disturbance, such as damage caused by a tropical cyclone. In particular, we compare a simple single species ordinary differential equation (ODE) model approach with a more complicated two-species coupled ODE model. Univariate profile likelihood analysis suggests that the both models are practically identifiable. To provide additional insight we construct and compare approximate prediction intervals using a new parameter-wise prediction approximation, confirming both the simple and complex models perform similarly with regard to making predictions. Our approximate parameter-wise prediction interval analysis provides explicit information about how each parameter affects the predictions of each model. Comparing our approximate prediction intervals with a more rigorous and computationally expensive evaluation of the full likelihood shows that the new approximations are reasonable in this case. All algorithms and software to support this work are freely available as jupyter notebooks on GitHub so that they can be adapted to deal with any other ODE-based models.

Epidemiology and Prognostic Indicators of Survival in Tongue Lymphoma.

OBJECTIVES: Lymphoma, categorized as either non-Hodgkin's lymphoma or Hodgkin's lymphoma, is the second most common malignancy in the head and neck. Primary tongue lymphoma is exceedingly rare, with only case reports or small case series in the literature. This population-based analysis is the first to report the epidemiology and prognostic factors of survival in patients with primary tongue lymphoma. METHODS: The Surveillance, Epidemiology, and End Results 18 database from the National Cancer Institute was queried for patients diagnosed between the years 2000 and 2016 with tongue lymphoma. Outcomes of interest were overall and disease-specific survival. Independent variables included age at diagnosis, sex, race, marital status, primary subsite, histologic subtype, stage, and treatment type. RESULTS AND CONCLUSION: Seven hundred forty patients met criteria; the male-female ratio was 1.5:1 and the mean age at diagnosis was 67.8 years. The majority of lesions localized to the base of tongue (90.0%), were histologically diffuse large B-cell lymphoma (59.5%), and presented at stage I or II (77.9%). Most early-stage lymphomas were treated with chemotherapy only (40.5%) or a combination of both chemotherapy and radiation (31.3%), while late-stage cancers were primarily treated with chemotherapy alone (68.5%). In multivariate analysis, younger age at diagnosis, female sex, married/partnered marital status, mucosa-associated lymphoid tissue histologic subtype, and earlier cancer stage were found to be associated with improved survival. Chemotherapy treatment with or without radiation was also associated with better survival compared to no treatment or radiation alone, though data regarding immunotherapy was unavailable.

Factors Shifting Preference Toward Telemedicine in the Delivery of Skin Cancer Reconstruction Care.

OBJECTIVES: Identify which delivery modality for skin reconstruction care, face-to-face (FTF) in-person versus two telemedicine modalities, store-and-forward (S&F) and live video chat (LVC), is patient preferred and how cost, access, wait time, and demographics influence this preference. STUDY DESIGN: Cross-sectional survey. METHODS: A 16-question survey querying demographics and five scenario-specific preferences questions for the delivery of skin cancer reconstruction care was created and distributed via Amazon Mechanical Turk (MTurk), a crowdsourcing online marketplace, and in-person to Mohs micrographic surgery patients. RESULTS: 1394 MTurk and 55 in-person responses were included. While 82.1% of online respondents prefer FTF clinic visits, this decreases to 58.3% with an in-person visit cost (p < 0.01) and furthermore to a minority 43.5% with both an in-person visit cost and wait time (p < 0.01) despite 77.8% believing that usefulness to the surgeon would improve FTF. Both the MTurk and in-person cohorts demonstrated similar response patterns despite considerable demographic differences. Multivariable analyses revealed that telemedicine was preferred by MTurk respondents with Medicaid (adjusted OR [95% CI]: 1.97 [1.18-3.31]) or Medicare (1.69 [1.10-2.59]) versus private insurance, and prior skin cancer (2.01 [1.18-3.42]) and less preferred by those earning $140,000+ per year (0.49 [0.29-0.82]) compared to those earning <$20,000 per year. CONCLUSIONS: FTF visits are preferred for skin cancer reconstruction care; this shifts toward virtual care with a cost and wait time in spite of the perceived quality of care. Individuals with socioeconomic barriers to access prefer telemedicine. MTurk can be a valuable tool for behavioral research in FPRS. LEVEL OF EVIDENCE: NA Laryngoscope, 133:294-301, 2023.

Computationally efficient framework for diagnosing, understanding and predicting biphasic population growth.

Throughout the life sciences, biological populations undergo multiple phases of growth, often referred to as biphasic growth for the commonly encountered situation involving two phases. Biphasic population growth occurs over a massive range of spatial and temporal scales, ranging from microscopic growth of tumours over several days, to decades-long regrowth of corals in coral reefs that can extend for hundreds of kilometres. Different mathematical models and statistical methods are used to diagnose, understand and predict biphasic growth. Common approaches can lead to inaccurate predictions of future growth that may result in inappropriate management and intervention strategies being implemented. Here, we develop a very general computationally efficient framework, based on profile likelihood analysis, for diagnosing, understanding and predicting biphasic population growth. The two key components of the framework are as follows: (i) an efficient method to form approximate confidence intervals for the change point of the growth dynamics and model parameters and (ii) parameter-wise profile predictions that systematically reveal the influence of individual model parameters on predictions. To illustrate our framework we explore real-world case studies across the life sciences.