Category: Statistics

J Med Internet Res. 2024 Jul 8;26:e51931. doi: 10.2196/51931.

ABSTRACT

BACKGROUND: Online appointment booking is a commonly used tool in several industries. There is limited evidence about the benefits and challenges of using online appointment booking in health care settings. Potential benefits include convenience and the ability to track appointments, although some groups of patients may find it harder to engage with online appointment booking. We sought to understand how patients in England used and experienced online appointment booking.

OBJECTIVE: This study aims to describe and compare the characteristics of patients in relation to their use of online appointment booking in general practice and investigate patients’ views regarding online appointment booking arrangements.

METHODS: This was a mixed methods study set in English general practice comprising a retrospective analysis of the General Practice Patient Survey (GPPS) and semistructured interviews with patients. Data used in the retrospective analysis comprised responses to the 2018 and 2019 GPPS analyzed using mixed-effects logistic regression. Semistructured interviews with purposively sampled patients from 11 general practices in England explored experiences of and views on online appointment booking. Framework analysis was used to allow for comparison with the findings of the retrospective analysis.

RESULTS: The retrospective analysis included 1,327,693 GPPS responders (2018-2019 combined). We conducted 43 interviews with patients with a variety of experiences and awareness of online appointment booking; of these 43 patients, 6 (14%) were from ethnic minority groups. In the retrospective analysis, more patients were aware that online appointment booking was available (581,224/1,288,341, 45.11%) than had experience using it (203,184/1,301,694, 15.61%). There were deprivation gradients for awareness and use and a substantial decline in both awareness and use in patients aged >75 years. For interview participants, age and life stage were factors influencing experiences and perceptions, working patients valued convenience, and older patients preferred to use the telephone. Patients with long-term conditions were more aware of (odds ratio [OR] 1.43, 95% CI 1.41-1.44) and more likely to use (OR 1.65, 95% CI 1.63-1.67) online appointment booking. Interview participants with long-term conditions described online appointment booking as useful for routine nonurgent appointments. Patients in deprived areas were clustered in practices with low awareness and use of online appointment booking among GPPS respondents (OR for use 0.65, 95% CI 0.64-0.67). Other key findings included the influence of the availability of appointments online and differences in the registration process for accessing online booking.

CONCLUSIONS: Whether and how patients engage with online appointment booking is influenced by the practice with which they are registered, whether they live with long-term conditions, and their deprivation status. These factors should be considered in designing and implementing online appointment booking and have implications for patient engagement with the wider range of online services offered in general practice.

PMID:38976870 | DOI:10.2196/51931

J Med Internet Res. 2024 Jul 8;26:e56110. doi: 10.2196/56110.

ABSTRACT

BACKGROUND: OpenAI’s ChatGPT is a pioneering artificial intelligence (AI) in the field of natural language processing, and it holds significant potential in medicine for providing treatment advice. Additionally, recent studies have demonstrated promising results using ChatGPT for emergency medicine triage. However, its diagnostic accuracy in the emergency department (ED) has not yet been evaluated.

OBJECTIVE: This study compares the diagnostic accuracy of ChatGPT with GPT-3.5 and GPT-4 and primary treating resident physicians in an ED setting.

METHODS: Among 100 adults admitted to our ED in January 2023 with internal medicine issues, the diagnostic accuracy was assessed by comparing the diagnoses made by ED resident physicians and those made by ChatGPT with GPT-3.5 or GPT-4 against the final hospital discharge diagnosis, using a point system for grading accuracy.

RESULTS: The study enrolled 100 patients with a median age of 72 (IQR 58.5-82.0) years who were admitted to our internal medicine ED primarily for cardiovascular, endocrine, gastrointestinal, or infectious diseases. GPT-4 outperformed both GPT-3.5 (P<.001) and ED resident physicians (P=.01) in diagnostic accuracy for internal medicine emergencies. Furthermore, across various disease subgroups, GPT-4 consistently outperformed GPT-3.5 and resident physicians. It demonstrated significant superiority in cardiovascular (GPT-4 vs ED physicians: P=.03) and endocrine or gastrointestinal diseases (GPT-4 vs GPT-3.5: P=.01). However, in other categories, the differences were not statistically significant.

CONCLUSIONS: In this study, which compared the diagnostic accuracy of GPT-3.5, GPT-4, and ED resident physicians against a discharge diagnosis gold standard, GPT-4 outperformed both the resident physicians and its predecessor, GPT-3.5. Despite the retrospective design of the study and its limited sample size, the results underscore the potential of AI as a supportive diagnostic tool in ED settings.

PMID:38976865 | DOI:10.2196/56110

Prehosp Emerg Care. 2024 Jul 8:1-12. doi: 10.1080/10903127.2024.2376757. Online ahead of print.

ABSTRACT

OBJECTIVES: This study assesses the feasibility, inter-rater reliability, and accuracy of using OpenAI’s ChatGPT-4 and Google’s Gemini Ultra large language models (LLMs), for Emergency Medical Services (EMS) quality assurance. The implementation of these LLMs for EMS quality assurance has the potential to significantly reduce the workload on medical directors and quality assurance staff by automating aspects of the processing and review of patient care reports. This offers the potential for more efficient and accurate and identification of areas requiring improvement, thereby potentially enhancing patient care outcomesMETHODS: Two expert human reviewers, ChatGPT GPT-4, and Gemini Ultra assessed and rated 150 consecutively sampled and anonymized prehospital records from 2 large urban EMS agencies for adherence to 2020 National Association of State EMS metrics for cardiac care. We evaluated the accuracy of scoring, inter-rater reliability, and review efficiency. The inter-rater reliability for the dichotomous outcome of each EMS metric was measured using the kappa statistic.RESULTS: Human reviewers showed high interrater reliability, with 91.2% agreement and a kappa coefficient, 0.782 (0.654-0.910). ChatGPT-4 achieved substantial agreement with human reviewers in EKG documentation and aspirin administration (76.2% agreement, kappa coefficient, 0.401 (0.334-0.468), but performance varied across other metrics. Gemini Ultra’s evaluation was discontinued due to poor performance. No significant differences were observed in median review times: 01:28 minutes (IQR 1:12 – 1:51 min) per human chart review, 01:24 minutes (IQR 01:09 – 01:53 min) per ChatGPT-4 chart review (p = 0.46), and 01:50 minutes (IQR 01:10-03:34 min) per Gemini Ultra review (p = 0.06).CONCLUSIONS: Large language models demonstrate potential in supporting quality assurance by effectively and objectively extracting data elements. However, their accuracy in interpreting non-standardized and time-sensitive details remains inferior to human evaluators. Our findings suggest that current LLMs may best offer supplemental support to the human review processes, but their value remains limited. Enhancements in LLM training and integration are recommended for improved and more reliable performance in the quality assurance processes.

PMID:38976859 | DOI:10.1080/10903127.2024.2376757

J Psychosoc Nurs Ment Health Serv. 2024 Jul;62(7):11-15. doi: 10.3928/02793695-20240620-01. Epub 2024 Jul 1.

ABSTRACT

The mental health needs of Asian American older adults are complex and multifaceted. Despite their rich diversity, Asian American older adults face significant challenges, including mental health stigma, cultural stress, limited English proficiency, and historical trauma. In addition, the coronavirus disease 2019 pandemic reignited preexisting anti-Asian attitudes of hostility, discrimination, blame, and scapegoating. The historical context of Asian immigration to the United States, impact of race-based discrimination, and recent resurgence of anti-Asian hate crimes impact mental health in Asian American older adults. Thus, there is a need for a culturally sensitive and competent mental health care workforce, culturally tailored interventions, and family involvement. In the context of research and policy, it is critical to prioritize increased funding and research focus on culturally tailored instrument development, interventions, and policy initiatives informed by recent findings to safeguard this population from hate crimes and discrimination. [Journal of Psychosocial Nursing and Mental Health Services, 62(7), 11-15.].

PMID:38976856 | DOI:10.3928/02793695-20240620-01

Orthopedics. 2024 Jul 10:1-5. doi: 10.3928/01477447-20240702-02. Online ahead of print.

ABSTRACT

BACKGROUND: Displaced diaphyseal fractures can be reduced using the push-pull technique, wherein a plate is affixed to the distal fragment of the fracture, a post screw is placed proximal to the plate, and a lamina spreader creates distraction. This study evaluated the load to failure and mechanism of failure of bicortical and unicortical post screws during reduction.

MATERIALS AND METHODS: Four matched pairs of cadaver legs were subjected to a 2-cm oblique osteotomy simulating a displaced, oblique diaphyseal fracture. A 6-hole compression plate was affixed to the distal fragment with 2 unicortical locking screws, and a 12-mm uni-cortical or 20-mm bicortical screw was inserted as a post screw proximal to the plate. A lamina bone spreader was used to exert a distraction force between the plate and the post screw. A mechanical actuator simulated the distraction procedure until failure. Maximum applied load, displacement, and absorbed energy were recorded and compared across unicortical and bicortical groups by paired t tests.

RESULTS: At maximum load, we found statistically significant differences in displacement (P=.003) and energy absorbed (P=.022) between the two groups. All unicortical screws failed through screw toggle and bone cut-out. Bicortical screws failed through bending, with no visible damage to the bone at the screw site.

CONCLUSION: When diaphyseal fractures are significantly shortened and require a greater distraction force to achieve reduction, bicortical screws demonstrate a higher mechanical load to failure and increased bone loss from the screw-removal site. A unicortical post screw may be used if minimal distraction is needed. [Orthopedics. 202x;4x(x):xx-xx.].

PMID:38976846 | DOI:10.3928/01477447-20240702-02

JCO Precis Oncol. 2024 Jun;8:e2300718. doi: 10.1200/PO.23.00718.

ABSTRACT

PURPOSE: To use modern machine learning approaches to enhance and automate the feature extraction from the longitudinal circulating tumor DNA (ctDNA) data and to improve the prediction of survival and disease progression, risk stratification, and treatment strategies for patients with 1L non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS: Using IMpower150 trial data on patients with untreated metastatic NSCLC treated with atezolizumab and chemotherapies, we developed a machine learning algorithm to extract predictive features from ctDNA kinetics, improving survival and progression prediction. We analyzed kinetic data from 17 ctDNA summary markers, including cell-free DNA concentration, allele frequency, tumor molecules in plasma, and mutation counts.

RESULTS: Three hundred and ninety-eight patients with ctDNA data (206 in training and 192 in validation) were analyzed. Our models outperformed existing workflow using conventional temporal ctDNA features, raising overall survival (OS) concordance index to 0.72 and 0.71 from 0.67 and 0.63 for C3D1 and C4D1, respectively, and substantially improving progression-free survival (PFS) to approximately 0.65 from the previous 0.54-0.58, a 12%-20% increase. Additionally, they enhanced risk stratification for patients with NSCLC, achieving clear OS and PFS separation. Distinct patterns of ctDNA kinetic characteristics (eg, baseline ctDNA markers, depth of ctDNA responses, and timing of ctDNA clearance, etc) were revealed across the risk groups. Rapid and complete ctDNA clearance appears essential for long-term clinical benefit.

CONCLUSION: Our machine learning approach offers a novel tool for analyzing ctDNA kinetics, extracting critical features from longitudinal data, improving our understanding of the link between ctDNA kinetics and progression/mortality risks, and optimizing personalized immunotherapies for 1L NSCLC.

PMID:38976829 | DOI:10.1200/PO.23.00718

J Chem Theory Comput. 2024 Jul 8. doi: 10.1021/acs.jctc.4c00514. Online ahead of print.

ABSTRACT

Alchemical free energy methods are useful in computer-aided drug design and computational protein design because they provide rigorous statistical mechanics-based estimates of free energy differences from molecular dynamics simulations. λ dynamics is a free energy method with the ability to characterize combinatorial chemical spaces spanning thousands of related systems within a single simulation, which gives it a distinct advantage over other alchemical free energy methods that are mostly limited to pairwise comparisons. Recently developed methods have improved the scalability of λ dynamics to perturbations at many sites; however, the size of chemical space that can be explored at each individual site has previously been limited to fewer than ten substituents. As the number of substituents increases, the volume of alchemical space corresponding to nonphysical alchemical intermediates grows exponentially relative to the size corresponding to the physical states of interest. Beyond nine substituents, λ dynamics simulations become lost in an alchemical morass of intermediate states. In this work, we introduce new biasing potentials that circumvent excessive sampling of intermediate states by favoring sampling of physical end points relative to alchemical intermediates. Additionally, we present a more scalable adaptive landscape flattening algorithm for these larger alchemical spaces. Finally, we show that this potential enables more efficient sampling in both protein and drug design test systems with up to 24 substituents per site, enabling, for the first time, simultaneous simulation of all 20 amino acids.

PMID:38976796 | DOI:10.1021/acs.jctc.4c00514

J Healthc Manag. 2024 Jul-Aug 01;69(4):280-295. doi: 10.1097/JHM-D-23-00137. Epub 2024 Jul 4.

ABSTRACT

GOAL: We sought to build upon previous studies that have demonstrated how healthcare workers’ ratings of their immediate supervisor’s leadership capabilities relate to their well-being and job satisfaction.

METHODS: In 2022, we analyzed cross-sectional data from 1,780 physicians and 39,896 allied health professionals (collected in 2017) and 729 residents (collected in 2019), as well as longitudinal data from 1,632 physicians (collected from 2015 to 2017), to identify a psychometrically strong, broadly applicable, actionable, and low-burden approach to assessing supervisor leadership capability to support healthcare worker well-being.

PRINCIPAL FINDINGS: The magnitude of association between our 1-, 2-, 3-, and 9-item leadership indexes and burnout, and between our 1-, 2-, 3-, and 9-item leadership indexes and satisfaction with the organization were similar to each other in the cross-sectional and longitudinal cohorts and across diverse groups of healthcare workers, including physicians, residents, and allied health professionals. The likelihood ratio for a high leadership score increased with an increasing score for each leadership measure. The area under the receiver operating characteristic curve for the 1-, 2-, and 3-item measures for a high leadership score was 0.9349, 0.9672, and 0.9819, respectively.

PRACTICAL APPLICATIONS: A single item assessing perceptions of leadership capability efficiently provides useful information about leadership qualities of healthcare workers’ immediate supervisors. The inclusion of this item in healthcare worker surveys may be useful for evaluating interventions and galvanizing organizational action to support healthcare worker well-being.

PMID:38976788 | DOI:10.1097/JHM-D-23-00137

PLoS Comput Biol. 2024 Jul 8;20(7):e1012287. doi: 10.1371/journal.pcbi.1012287. Online ahead of print.

ABSTRACT

Identifying the association and corresponding types of miRNAs and diseases is crucial for studying the molecular mechanisms of disease-related miRNAs. Compared to traditional biological experiments, computational models can not only save time and reduce costs, but also discover potential associations on a large scale. Although some computational models based on tensor decomposition have been proposed, these models usually require manual specification of numerous hyperparameters, leading to a decrease in computational efficiency and generalization ability. Additionally, these linear models struggle to analyze complex, higher-order nonlinear relationships. Based on this, we propose a novel framework, KBLTDARD, to identify potential multiple types of miRNA-disease associations. Firstly, KBLTDARD extracts information from biological networks and high-order association network, and then fuses them to obtain more precise similarities of miRNAs (diseases). Secondly, we combine logistic tensor decomposition and Bayesian methods to achieve automatic hyperparameter search by introducing sparse-induced priors of multiple latent variables, and incorporate auxiliary information to improve prediction capabilities. Finally, an efficient deterministic Bayesian inference algorithm is developed to ensure computational efficiency. Experimental results on two benchmark datasets show that KBLTDARD has better Top-1 precision, Top-1 recall, and Top-1 F1 for new type predictions, and higher AUPR, AUC, and F1 values for new triplet predictions, compared to other state-of-the-art methods. Furthermore, case studies demonstrate the efficiency of KBLTDARD in predicting multiple types of miRNA-disease associations.

PMID:38976761 | DOI:10.1371/journal.pcbi.1012287

Gynecol Endocrinol. 2024 Dec;40(1):2375568. doi: 10.1080/09513590.2024.2375568. Epub 2024 Jul 8.

ABSTRACT

BACKGROUND: Iron metabolism plays a significant role in the development of metabolic disorders in women with polycystic ovary syndrome (PCOS). Despite the importance of hepcidin, a key iron regulator, current research on serum hepcidin levels in PCOS patients shows conflicting results.

METHODS: PubMed, Embase, Web of Science, Cochrane Library and the China National Knowledge Infrastructure (CNKI) database were systematically searched from their inception to 9 September 2023. The search aimed to identify studies in English and Chinese that examined hepcidin levels in women with PCOS compared to healthy control subjects. Standardized mean differences (SMDs) with corresponding 95% confidence intervals (95% CIs) were calculated to evaluate the difference in serum hepcidin levels between women with and without PCOS.

RESULTS: The meta-analysis included a total of 10 eligible studies, which encompassed 499 PCOS patients and 391 control subjects. The pooled analysis revealed a significant reduction in serum hepcidin levels among the PCOS patients compared to the healthy controls (SMD = -3.49, 95% CI: -4.68 to -2.30, p < .05). There was no statistically significant difference in serum hepcidin levels between PCOS patients with a body mass index (BMI) < 25 and those with a BMI ≥ 25 (p > .05).

CONCLUSION: The serum hepcidin levels of women with PCOS were significantly lower than those of healthy controls, which suggests that serum hepcidin could be a potential biomarker for PCOS.

PMID:38976752 | DOI:10.1080/09513590.2024.2375568