Data needs for sea otter bioenergetics modeling.

Sea otters are keystone predators whose recovery and expansion from historical exploitation throughout their range can serve to enhance local biodiversity, promote community stability, and buffer against habitat loss in nearshore marine systems. Bioenergetics models have become a useful tool in conservation and management efforts of marine mammals generally, yet no bioenergetics model exists for sea otters. Previous research provides abundant data that can be used to develop bioenergetics models for this species, yet important data gaps remain. Here we review the available data that could inform a bioenergetics model, and point to specific open questions that could be answered to more fully inform such an effort. These data gaps include quantifying energy intake through foraging by females with different aged pups in different quality habitats, the influence of body size on energy intake through foraging, and determining the level of fat storage that is possible in sea otters of different body sizes. The more completely we fill these data gaps, the more confidence we can have in the results and predictions produced by future bioenergetics modeling efforts for this species.

Patterns of nitrate load variability under surface water-groundwater interactions in agriculturally intensive valley watersheds.

Nitrate pollution is a significant environmental issue closely related to human activities, complicated hydrological interactions and nitrate fate in the valley watershed strongly affects nitrate load in hydrological systems. In this study, a nitrate reactive transport model by coupling SWAT-MODFLOW-RT3D between surface water and groundwater interactions at the watershed scale was developed, which was used to reproduce the interaction between surface water and groundwater in the basin from 2016 to 2019 and to reveal the nitrogen transformation process and the evolving trend of nitrate load within the hydrological system of the valley watershed. The results showed that the basin exhibited groundwater recharge to surface water in 2016-2019, particularly in the northwestern and northeastern mountainous regions of the valley watershed and the southern Beishan Reservoir vicinity. Groundwater recharge to surface water declined by 20.17 % from 2016 to 2019 due to precipitation. Nitrate loads in the hydrologic system of the watershed are primarily derived from human activities (including fertilizer application from agricultural activities and residential wastewater discharges) and the nitrogen cycle. Nitrate loads in surface water declined 16.05 % from 2016 to 2019. Nitrate levels are higher in agricultural farming and residential areas on the eastern and northern sides of the watershed. Additionally, hydrological interactions are usually accompanied by material accumulation and environmental changes. Nitrate levels tend to rise with converging water flows, a process that becomes more pronounced during precipitation events and cropping seasons in agriculturally intensive valley watersheds. However, environmental changes alter nitrogen transformation processes. Nitrogen fixation, nitrification, and ammonification intensify nitrogen inputs during river pooling, enhancing nitrogen cycling fluxes and elevating nitrate loads. These processes are further enhanced during groundwater recharge to surface water, leading to evaluated nitrate load. Enhanced denitrification, dissimilatory nitrate reduction to ammonium (DNRA), anaerobic ammonia oxidation, and assimilation promote the nitrogen export from the system and reduce the nitrate load during surface water recharge to groundwater.

Five steps in performing machine learning for binary outcomes.

BACKGROUND: The use of machine learning (ML) in cardiovascular and thoracic surgery is evolving rapidly. Maximizing the capabilities of ML can help improve patient risk stratification and clinical decision making, improve accuracy of predictions, and improve resource utilization in cardiac surgery. The many nuances and intricacies of ML modeling need to be understood to appropriately implement these technologies in the clinical research setting. This primer provides an educational framework of ML for generating predicted probabilities in clinical research and illustrates it with a real-world clinical example. METHODS: We focus on modeling for binary classification and imbalanced classes, a common scenario in cardiothoracic surgery research. We present a 5-step strategy for successfully harnessing the power of ML and performing such analyses, and demonstrate our strategy using a real-world example based on data from the National Surgical Quality Improvement Program pediatric database. CONCLUSIONS: Collaboration among surgeons, care providers, statisticians, data scientists, and information technology professionals can help to maximize the impact of ML as a powerful tool in cardiac surgery.

Development model based on visual image big data applied to art management.

This paper aims to explore the application of visual image big data (BD) in art management, and proposes and develops a new art management model. First of all, this study conducted extensive research on the overview and application of big data, focusing on analyzing the characteristics of big data and its characteristics and application methods in art management. By introducing image processing (IP) technology, this paper expounds on the application of visual image technology in art management in detail and discusses the classification of computer vision images to determine its application direction. On this basis, this paper proposes the application of visual images and big data in art management from three aspects: the accurate acquisition of visual images, the development model of art management, and the development of visual image technology in art resource management and teaching, and strengthens the development model of art management based on IP algorithm. Experiments and surveys show that the art management model development system built by the newly introduced visual image technology, big data technology, and IP algorithm can increase user satisfaction by 24 %. This result shows that the new model has a significant effect in improving the efficiency and quality of art management, providing strong technical support for the field of art management, while also providing designers with a more accurate tool for assessing market trends, helping to adhere to and promote good design concepts.

Assessment of health technology acceptability for remote monitoring of patients with COVID-19: A measurement model for user perceptions of pulse oximeters.

Objective: This study aims to develop a measurement model for health technology acceptability using a theoretical framework and a range of validated instruments to measure user experience, acceptance, usability, health and digital health literacy. Methods: A cross-sectional evaluation study using a mixed-methods approach was conducted. An online survey was administered to patients who used a pulse oximeter in a virtual hospital setting during COVID-19. The model development was conducted in three steps: (1) exploratory factor analysis for conceptual model development, (2) measurement model confirmation through confirmatory factor analysis followed by structural equation modelling and (3) test of model external validity on four outcome measures. Finally, the different constructs of the developed model were used to compare two types of pulse oximeters by measuring the standardised scores. Results: Two hundred and two participants were included in the analysis, 37.6% were female and the average age was 53 years (SD:15.38). A four-construct model comprising Task Load, Affective Attitude, Self-Efficacy and Value of Use (0.636-0.857 factor loadings) with 12 items resulted from the exploratory factor analysis and yielded a good fit (RMSEA = .026). Health and digital health literacy did not affect the overall reliability of the model. Frustration, performance, trust and satisfaction were identified as outcomes of the model. No significant differences were observed in the acceptability constructs when comparing the two pulse oximeter devices. Conclusions: This article proposes a model for the measurement of the acceptability of health technologies used by patients in a remote care setting based on the use of a pulse oximeter in COVID-19 remote monitoring.

Quantifying the Abundance of Alkane Moieties in Lignins with FTIR Spectroscopy and PLS Regression; Estimating Grafting Degree of Esterification.

As society is rapidly converting from fossil-based materials to greener alternatives, the valorization of lignin through chemical modification has been given considerable attention. Characterizing this highly heterogeneous biopolymer is a constant challenge, and an emerging strategy for dealing with variations in material characteristics is combining traditional analytical techniques with chemometrics, such as Fourier-transform infrared (FTIR) spectroscopy with partial least squares regression (PLSR). Here, a calibration data set was built based on FTIR spectra and the total carbon-hydrogen bond (CHB) content of mixtures of technical lignins and alkanes, meant to emulate esterified samples. From this data, a PLSR model was built which predicted the CHB content of esterified lignin reaction products with an RMSECV = 5.685 mmol/g and RMSEPred = 5.827 mmol/g, and from which the weight percentage of ester-to-lignin was determined. When compared to wet-chemical analysis, good agreement between the techniques was found with an obtained RMSEPred = 8.3% and a R2Train = 0.9752 for the degree of esterification (DoE). This indicates high model predictability and goodness of fit, and that the calibration data set successfully emulated esterified lignin samples.

Evaluating Inflammatory Bowel Disease-Related Quality of Life Using an Interpretable Machine Learning Approach: A Multicenter Study in China.

Purpose: Impaired quality of life (QOL) is common in patients with inflammatory bowel disease (IBD). A tool to more quickly identify IBD patients at high risk of impaired QOL improves opportunities for earlier intervention and improves long-term prognosis. The purpose of this study was to use a machine learning (ML) approach to develop risk stratification models for evaluating IBD-related QOL impairments. Patients and Methods: An online questionnaire was used to collect clinical data on 2478 IBD patients from 42 hospitals distributed across 22 provinces in China from September 2021 to May 2022. Eight ML models used to predict the risk of IBD-related QOL impairments were developed and validated. Model performance was evaluated using a set of indexes and the best ML model was explained using a Local Interpretable Model-Agnostic Explanations (LIME) algorithm. Results: The support vector machine (SVM) classifier algorithm-based model outperformed other ML models with an area under the receiver operating characteristic curve (AUC) and an accuracy of 0.80 and 0.71, respectively. The feature importance calculated by the SVM classifier algorithm revealed that glucocorticoid use, anxiety, abdominal pain, sleep disorders, and more severe disease contributed to a higher risk of impaired QOL, while longer disease course and the use of biological agents and immunosuppressants were associated with a lower risk. Conclusion: An ML approach for assessing IBD-related QOL impairments is feasible and effective. This mechanism is a promising tool for gastroenterologists to identify IBD patients at high risk of impaired QOL.

Tutorial: dos and don'ts in clinical prediction research for venous thromboembolism.

Clinical prediction modeling has become an increasingly popular domain of venous thromboembolism research in recent years. Prediction models can help healthcare providers make decisions regarding starting or withholding therapeutic interventions, or referrals for further diagnostic workup, and can form a basis for risk stratification in clinical trials. The aim of the current guide is to assist in the practical application of complicated methodological requirements for well-performed prediction research by presenting key dos and don'ts while expanding the understanding of predictive research in general for (clinical) researchers who are not specifically trained in the topic; throughout we will use prognostic venous thromboembolism scores as an exemplar.

Development and Characterization of Syngeneic Orthotopic Transplant Models of Obesity-Responsive Triple-Negative Breast Cancer in C57BL/6J Mice.

Obesity is an established risk and progression factor for triple-negative breast cancer (TNBC), but preclinical studies to delineate the mechanisms underlying the obesity-TNBC link as well as strategies to break that link are constrained by the lack of tumor models syngeneic to obesity-prone mouse strains. C3(1)/SV40 T-antigen (C3-TAg) transgenic mice on an FVB genetic background develop tumors with molecular and pathologic features that closely resemble human TNBC, but FVB mice are resistant to diet-induced obesity (DIO). Herein, we sought to develop transplantable C3-TAg cell lines syngeneic to C57BL/6 mice, an inbred mouse strain that is sensitive to DIO. We backcrossed FVB-Tg(C3-1-TAg)cJeg/JegJ to C57BL/6 mice for ten generations, and spontaneous tumors from those mice were excised and used to generate four clonal cell lines (B6TAg1.02, B6TAg2.03, B6TAg2.10, and B6TAg2.51). We characterized the growth of the four cell lines in both lean and DIO C57BL/6J female mice and performed transcriptomic profiling. Each cell line was readily tumorigenic and had transcriptional profiles that clustered as claudin-low, yet markedly differed from each other in their rate of tumor progression and transcriptomic signatures for key metabolic, immune, and oncogenic signaling pathways. DIO accelerated tumor growth of orthotopically transplanted B6TAg1.02, B6TAg2.03, and B6TAg2.51 cells. Thus, the B6TAg cell lines described herein offer promising and diverse new models to augment the study of DIO-associated TNBC.

A BERT-based approach for identifying anti-inflammatory peptides using sequence information.

The use of anti-inflammatory peptides (AIPs) as an alternative therapeutic approach for inflammatory diseases holds great research significance. Due to the high cost and difficulty in identifying AIPs with experimental methods, the discovery and design of peptides by computational methods before the experimental stage have become promising technology. In this study, we present BertAIP, a bidirectional encoder representation from transformers (BERT)-based method for predicting AIPs directly from their amino acid sequence without using any other information. BertAIP implements a BERT model to extract features of a protein, and uses a fully connected feed-forward network for AIP classification. It was constructed and evaluated using the AIP datasets that were reconstructed from the latest Immune Epitope Database. The experimental results showed that BertAIP achieved an accuracy of 0.751 and a Matthews correlation coefficient of 0.451, which were higher than other commonly used methods. The results of the independent test suggested that BertAIP outperformed the existing AIP predictors. In addition, to enhance the interpretability of BertAIP, we explored and visualized the amino acids that the model considered important for AIP prediction. We believe that the BertAIP proposed herein will be a useful tool for large-scale screening and identifying novel AIPs for drug development and therapeutic research related to inflammatory diseases.

Porcine model of a complicated skin and soft tissue infection caused by Pseudomonas aeruginosa.

Pseudomonas aeruginosa poses a significant threat to both immunocompetent and immunocompromised individuals, often resulting in life-threatening infections. With increasing antimicrobial resistance, novel therapeutic strategies are urgently needed. Although animal models are crucial for preclinical studies, limited data are available for porcine models, more specifically for P. aeruginosa complicated skin and soft tissue infections (cSSTIs). This study presents a novel porcine model inducing and sustaining cSSTI for 14 days. Six pigs (120 wounds) were used for the development of infections, and within this group, two pigs (40 wounds) were used to evaluate the progression of the cSSTI infection. The model demonstrated bacterial loads of more than 107 CFU/gram of tissue or higher. The cSSTI fully developed within three days and remained well above these levels until day 14 post-infection. Due to the immunocompetence of this model, all the immunological processes associated with the response to the presence of infection and the wound healing process are preserved.

Development and validation of machine learning models to predict frailty risk for elderly.

AIMS: Early identification and intervention of the frailty of the elderly will help lighten the burden of social medical care and improve the quality of life of the elderly. Therefore, we used machine learning (ML) algorithm to develop models to predict frailty risk in the elderly. DESIGN: A prospective cohort study. METHODS: We collected data on 6997 elderly people from Chinese Longitudinal Healthy Longevity Study wave 6-7 surveys (2011-2012, 2014). After the baseline survey in 1998 (wave 1), the project conducted follow-up surveys (wave 2-8) in 2000-2018. The osteoporotic fractures index was used to assess frailty. Four ML algorithms (random forest [RF], support vector machine, XGBoost and logistic regression [LR]) were used to develop models to identify the risk factors of frailty and predict the risk of frailty. Different ML models were used for the prediction of frailty risk in the elderly and frailty risk was trained on a cohort of 4385 elderly people with frailty (split into a training cohort [75%] and internal validation cohort [25%]). The best-performing model for each study outcome was tested in an external validation cohort of 6997 elderly people with frailty pooled from the surveys (wave 6-7). Model performance was assessed by receiver operating curve and F2-score. RESULTS: Among the four ML models, the F2-score values were similar (0.91 vs. 0.91 vs. 0.88 vs. 0.90), and the area under the curve (AUC) values of RF model was the highest (0.75), followed by LR model (0.74). In the final two models, the AUC values of RF and LR model were similar (0.77 vs. 0.76) and their accuracy was identical (87.4% vs. 87.4%). CONCLUSION: Our study developed a preliminary prediction model based on two different ML approaches to help predict frailty risk in the elderly. IMPACT: The presented models from this study can be used to inform healthcare providers to predict the frailty probability among older adults and maybe help guide the development of effective frailty risk management interventions. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Detecting frailty at an early stage and implementing timely targeted interventions may help to improve the allocation of health care resources and to reduce frailty-related burden. Identifying risk factors for frailty could be beneficial to provide tailored and personalized care intervention for older adults to more accurately prevent or improve their frail conditions so as to improve their quality of life. REPORTING METHOD: The study has adhered to STROBE guidelines. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Guidelines to support newly qualified professional nurses for effective clinical practice.

BACKGROUND:  Transitioning to a professional role is difficult for newly qualified professional nurses. Given the challenges that these nurses experience during the transition to practice, support is essential for them to become efficient, safe, confident, and competent in their professional roles. OBJECTIVES:  The purpose of this study was to explore the transition experiences of newly qualified professional nurses to develop a preceptorship model. METHOD:  This study employed a qualitative approach to purposively collect data. Concept analyses were conducted applying the steps suggested by Walker and Avant, and the related concepts were classified utilising the survey list of Dickoff, James and Wiedenbach's practice theory. RESULTS:  A preceptorship model for the facilitation of guidance and support in the clinical area for newly qualified professional nurses was developed. The model consists of six components, namely, the clinical environment, the operational manager and preceptor, the newly qualified professional nurse, the preceptorship, the assessment of learning, and the outcome. CONCLUSION:  The study revealed that newly qualified professional nurses face many transition challenges when entering clinical practice. They are thrown far in, experience a reality shock, and are not ready to start performing their professional role. The participants agreed that guidance and support are needed for their independent practice role.Contribution: The preceptorship model for newly qualified professional nurses would be necessary for the transition period within hospitals. This preceptorship model may be implemented by nursing education institutions as part of their curriculum to prepare pre-qualifying students for the professional role.

Designing eHealth interventions for children with complex care needs requires continuous stakeholder collaboration and co-creation.

Objective: Hospital-to-home (H2H) transitions challenge families of children with medical complexity (CMC) and healthcare professionals (HCP). This study aimed to gain deeper insights into the H2H transition process and to work towards eHealth interventions for its improvement, by applying an iterative methodology involving both CMC families and HCP as end-users. Methods: For 20-weeks, the Dutch Transitional Care Unit consortium collaborated with the Amsterdam University of Applied Sciences, HCP, and CMC families. The agile SCREAM approach was used, merging Design Thinking methods into five iterative sprints to stimulate creativity, ideation, and design. Continuous communication allowed rapid adaptation to new information and the refinement of solutions for subsequent sprints. Results: This iterative process revealed three domains of care - care coordination, social wellbeing, and emotional support - that were important to all stakeholders. These domains informed the development of our final prototype, 'Our Care Team', an application tailored to meet the H2H transition needs for CMC families and HCP. Conclusion: Complex processes like the H2H transition for CMC families require adaptive interventions that empower all stakeholders in their respective roles, to promote transitional care that is anticipatory, rather than reactive. Innovation: A collaborative methodology is needed, that optimizes existing resources and knowledge, fosters innovation through collaboration while using creative digital design principles. This way, we might be able to design eHealth solutions with end-users, not just for them.

Novel Approach for Detecting Respiratory Syncytial Virus in Pediatric Patients Using Machine Learning Models Based on Patient-Reported Symptoms: Model Development and Validation Study.

BACKGROUND: Respiratory syncytial virus (RSV) affects children, causing serious infections, particularly in high-risk groups. Given the seasonality of RSV and the importance of rapid isolation of infected individuals, there is an urgent need for more efficient diagnostic methods to expedite this process. OBJECTIVE: This study aimed to investigate the performance of a machine learning model that leverages the temporal diversity of symptom onset for detecting RSV infections and elucidate its discriminatory ability. METHODS: The study was conducted in pediatric and emergency outpatient settings in Japan. We developed a detection model that remotely confirms RSV infection based on patient-reported symptom information obtained using a structured electronic template incorporating the differential points of skilled pediatricians. An extreme gradient boosting-based machine learning model was developed using the data of 4174 patients aged ≤24 months who underwent RSV rapid antigen testing. These patients visited either the pediatric or emergency department of Yokohama City Municipal Hospital between January 1, 2009, and December 31, 2015. The primary outcome was the diagnostic accuracy of the machine learning model for RSV infection, as determined by rapid antigen testing, measured using the area under the receiver operating characteristic curve. The clinical efficacy was evaluated by calculating the discriminative performance based on the number of days elapsed since the onset of the first symptom and exclusion rates based on thresholds of reasonable sensitivity and specificity. RESULTS: Our model demonstrated an area under the receiver operating characteristic curve of 0.811 (95% CI 0.784-0.833) with good calibration and 0.746 (95% CI 0.694-0.794) for patients within 3 days of onset. It accurately captured the temporal evolution of symptoms; based on adjusted thresholds equivalent to those of a rapid antigen test, our model predicted that 6.9% (95% CI 5.4%-8.5%) of patients in the entire cohort would be positive and 68.7% (95% CI 65.4%-71.9%) would be negative. Our model could eliminate the need for additional testing in approximately three-quarters of all patients. CONCLUSIONS: Our model may facilitate the immediate detection of RSV infection in outpatient settings and, potentially, in home environments. This approach could streamline the diagnostic process, reduce discomfort caused by invasive tests in children, and allow rapid implementation of appropriate treatments and isolation at home. The findings underscore the potential of machine learning in augmenting clinical decision-making in the early detection of RSV infection.

The development and internal validation of a multivariable model predicting 6-month mortality for people with opioid use disorder presenting to community drug services in England: a protocol.

BACKGROUND: People with opioid use disorder have substantially higher standardised mortality rates compared to the general population; however, lack of clear individual prognostic information presents challenges to prioritise or target interventions within drug treatment services. Previous prognostic models have been developed to estimate the risk of developing opioid use disorder and opioid-related overdose in people routinely prescribed opioids but, to our knowledge, none have been developed to estimate mortality risk in people accessing drug services with opioid use disorder. Initial presentation to drug services is a pragmatic time to evaluate mortality risk given the contemporaneous routine collection of prognostic indicators and as a decision point for appropriate service prioritisation and targeted intervention delivery. This study aims to develop and internally validate a model to estimate 6-month mortality risk for people with opioid use disorder from prognostic indicators recorded at initial assessment in drug services in England. METHODS: An English national dataset containing records from individuals presenting to drug services between 1 April 2013 and 1 April 2023 (n > 800,000) (the National Drug Treatment Monitoring System (NDTMS)) linked to their lifetime hospitalisation and death records (Hospital Episode Statistics-Office of National Statistics (HES-ONS)). Twelve candidate prognostic indicator variables were identified based on literature review of demographic and clinical features associated with increased mortality for people in treatment for opioid use disorder. Variables will be extracted at initial presentation to drug services with mortality measured at 6 months. Two multivariable Cox regression models will be developed one for 6-month all-cause mortality and one for 6-month drug-related mortality using backward elimination with a fractional polynomial approach for continuous variables. Internal validation will be undertaken using bootstrapping methods. Discrimination of both models will be reported using Harrel's c and d-statistics. Calibration curves and slopes will be presented comparing expected and observed event rates. DISCUSSION: The models developed and internally validated in this study aim to improve clinical assessment of mortality risk for people with opioid use disorder presenting to drug services in England. External validation in different populations will be required to develop the model into a tool to assist future clinical decision-making.

Creating High-Quality Synthetic Health Data: Framework for Model Development and Validation.

BACKGROUND: Electronic health records are a valuable source of patient information that must be properly deidentified before being shared with researchers. This process requires expertise and time. In addition, synthetic data have considerably reduced the restrictions on the use and sharing of real data, allowing researchers to access it more rapidly with far fewer privacy constraints. Therefore, there has been a growing interest in establishing a method to generate synthetic data that protects patients' privacy while properly reflecting the data. OBJECTIVE: This study aims to develop and validate a model that generates valuable synthetic longitudinal health data while protecting the privacy of the patients whose data are collected. METHODS: We investigated the best model for generating synthetic health data, with a focus on longitudinal observations. We developed a generative model that relies on the generalized canonical polyadic (GCP) tensor decomposition. This model also involves sampling from a latent factor matrix of GCP decomposition, which contains patient factors, using sequential decision trees, copula, and Hamiltonian Monte Carlo methods. We applied the proposed model to samples from the MIMIC-III (version 1.4) data set. Numerous analyses and experiments were conducted with different data structures and scenarios. We assessed the similarity between our synthetic data and the real data by conducting utility assessments. These assessments evaluate the structure and general patterns present in the data, such as dependency structure, descriptive statistics, and marginal distributions. Regarding privacy disclosure, our model preserves privacy by preventing the direct sharing of patient information and eliminating the one-to-one link between the observed and model tensor records. This was achieved by simulating and modeling a latent factor matrix of GCP decomposition associated with patients. RESULTS: The findings show that our model is a promising method for generating synthetic longitudinal health data that is similar enough to real data. It can preserve the utility and privacy of the original data while also handling various data structures and scenarios. In certain experiments, all simulation methods used in the model produced the same high level of performance. Our model is also capable of addressing the challenge of sampling patients from electronic health records. This means that we can simulate a variety of patients in the synthetic data set, which may differ in number from the patients in the original data. CONCLUSIONS: We have presented a generative model for producing synthetic longitudinal health data. The model is formulated by applying the GCP tensor decomposition. We have provided 3 approaches for the synthesis and simulation of a latent factor matrix following the process of factorization. In brief, we have reduced the challenge of synthesizing massive longitudinal health data to synthesizing a nonlongitudinal and significantly smaller data set.

Developing a model describing voluntary residency attrition: a qualitative study.

BACKGROUND: Many factors influencing residency attrition are identified in the literature, but what role these factors play and how they influence each other remains unclear. Understanding more about the interaction between these factors can provide background to put the available evidence into perspective and provide tools to reduce attrition. The aim of this study was therefore to develop a model that describes voluntary residency attrition. METHODS: Semi-structured interviews were held with a convenient sample of orthopaedic surgery residents in the Netherlands who dropped out of training between 2000 and 2018. Transcripts were analysed using a constructivist grounded theory approach. Concepts and themes were identified by iterative constant comparison. RESULTS: Seventeen interviews with former residents were analysed and showed that reasons for voluntary attrition were different for each individual and often a result of a cumulative effect. Individual expectations and needs determine residents' experiences with the content of the profession, the professional culture and the learning climate. Personal factors like previous clinical experiences, personal circumstances and personal characteristics influence expectations and needs. Specific aspects of the residency programme contributing to attrition were type of patient care, required skills for the profession, work-life balance and interpersonal interaction. CONCLUSIONS: This study provides a model for voluntary resident attrition showing the factors involved and how they interact. This model places previous research into perspective, gives implications for practice on the (im)possibilities of preventing attrition and opens possibilities for further research into resident attrition.

Standardized approach for accurate and reliable model development of ion-exchange chromatography based on parameter-by-parameter method and consideration of extra-column effects.

Developing an accurate and reliable model for chromatographic separation that meets regulatory requirements and ensures consistency in model development remains challenging. In order to address this challenge, a standardized approach was proposed in this study with ion-exchange chromatography (IEC). The approach includes the following steps: liquid flow identification, system and column-specific parameters determination and validation, multi-component system identification, protein amount validation, steric mass action parameters determination and evaluation, and validation of the calibrated model's generalization ability. The parameter-by-parameter (PbP) calibration method and the consideration of extra-column effects were integrated to enhance the accuracy of the developed models. The experiments designed for implementing the PbP method (five gradient experiments for model calibration and one stepwise experiment for model validation) not only streamline the experimental workload but also ensure the extrapolation abilities of the model. The effectiveness of the standardized approach is successfully validated through an application about the IEC separation of industrial antibody variants, and satisfactory results were observed with R2 ≈ 0.9 for the majority of calibration and validation experiments. The standardized approach proposed in this work contributes significantly to improve the accuracy and reliability of the developed IEC models. Models developed using this standardized approach are ready to be applied to a broader range of industrial separation systems, and are likely find further applications in model-assisted decision-making of process development.

Comparing Predictive Performance of Time Invariant and Time Variant Clinical Prediction Models in Cardiac Surgery.

Clinical prediction models are increasingly used across healthcare to support clinical decision making. Existing methods and models are time-invariant and thus ignore the changes in populations and healthcare practice that occur over time. We aimed to compare the performance of time-invariant with time-variant models in UK National Adult Cardiac Surgery Audit data from Manchester University NHS Foundation Trust between 2009 and 2019. Data from 2009-2011 were used for initial model fitting, and data from 2012-2019 for validation and updating. We fitted four models to the data: a time-invariant logistic regression model (not updated), a logistic model which was updated every year and validated it in each subsequent year, a logistic regression model where the intercept is a function of calendar time (not updated), and a continually updating Bayesian logistic model which was updated with each new observation and continuously validated. We report predictive performance over the complete validation cohort and for each year in the validation data. Over the complete validation data, the Bayesian model had the best predictive performance.