Nevin Manimala

J Radiol Prot. 2025 Nov 25. doi: 10.1088/1361-6498/ae23d9. Online ahead of print.

ABSTRACT

Breast tissue is highly sensitive to ionizing radiation, making dose management in mammography crucial to reducing the risk of radiation-induced cancer and hereditary effects. Dose optimisation, guided by the ALARA principle, aims to minimise exposure while maintaining diagnostic quality. This study focuses on establishing National Diagnostic Reference Levels (NDRLs) for digital mammography in Nepal to support dose optimisation efforts. A retrospective analysis was conducted using data from 786 patients across six hospitals equipped with digital mammography systems. Both symptomatic and screening mammograms in cranial-caudal (CC) and mediolateral oblique (MLO) views were included for both breasts. Mean glandular dose (MGD) and entrance skin dose (ESD) were extracted from DICOM headers. For each mammogram view, data from a minimum of 50 patients were analysed. Technical parameters such as tube voltage (kVp), tube current (mAs), compression force (CF), and compressed breast thickness (CBT) were also documented. The established NDRLs for digital mammography are 1.03 mGy (RCC), 1.02 mGy (LCC), 1.18 mGy (RMLO), and 1.15 mGy (LMLO). The mean CBT and CF are 56±13 mm and 122±29 N, respectively. The overall NDRLs for CC and MLO views are 1.03 mGy and 1.17 mGy. Comparisons with other countries highlight the potential for further dose optimization to maintain diagnostically adequate images at lower exposure levels. Implementing such strategies can reduce patient radiation dose in digital mammography without compromising diagnostic performance.

PMID:41289610 | DOI:10.1088/1361-6498/ae23d9

JMIR Med Educ. 2025 Nov 25;11:e78363. doi: 10.2196/78363.

ABSTRACT

BACKGROUND: Learner autonomy-the ability to self-direct and regulate learning-is a key determinant of success in online education, yet its quantifiable impact in voluntary noncredit courses remains unclear. Understanding how autonomy translates into measurable behaviors and outcomes in clinical skills training may inform more effective online learning design and learning outcomes.

OBJECTIVE: This study aims to quantify the association between behavioral indicators of learner autonomy and performance in a voluntary noncredit online cardiac auscultation course.

METHODS: We conducted a prospective, self‑controlled, single‑center study. A total of 199 registrants (n=122 physicians and n=77 medical students) were recruited via WeChat and attended four weekly 2‑hour synchronous sessions using authentic patient heart sound recordings with imaging‑based explanations. The primary outcome was the final posttraining quiz score (0-100); training effectiveness was assessed by the pre‑ to posttraining score change. The autonomy indicators were full participation (attendance at all four sessions), in‑class engagement (number of responses to brief content‑aligned prompts posed approximately every 10-15 minutes; responses recorded for participation monitoring only), and postclass review (frequency/duration of reviewing recordings and materials). Analyses included Wilcoxon signed rank tests, χ² tests, multivariable linear regression, and receiver operating characteristic profiling of “excellent learners” (top 10% improvement).

RESULTS: Of the 199 registrants, 146 (73.4%) attended ≥1 session and 46 (23.1%) completed all sessions. Median test scores improved from 40 (IQR 20-50) to 70 (IQR 50-83; P<.001). Intrinsic motivation was associated with full participation (χ²₁=4.03; P=.045). In multivariable models, full participation (unstandardized B=41.55, 95% CI 24.43-58.67; standardized β=0.60; P<.001) and in‑class engagement (B=4.79 per additional response, 95% CI 3.05-6.45; β=0.70; P<.001) independently predicted higher final scores (adjusted R²=0.48). Receiver operating characteristic profiling indicated that greater postclass review (recordings/materials) led to learners achieving excellent performance.

CONCLUSIONS: In this voluntary online clinical skills course, showing up consistently, engaging during class, and reviewing after class-practical expressions of learner autonomy-were key correlates of short-term performance. These behaviors may be encouraged through simple, feasible course designs such as clear expectation setting, periodic interactive prompts, and structured review opportunities, which warrant prospective evaluation in future studies.

PMID:41289585 | DOI:10.2196/78363

JMIR Aging. 2025 Nov 25;8:e77227. doi: 10.2196/77227.

ABSTRACT

BACKGROUND: While the positive effects of digital technology on cognitive function are established, the specific impacts of different types of technology activities on distinct cognitive domains remain underexplored.

OBJECTIVE: This study aimed to examine the associations between transitions into and out of various technology activities and trajectories of cognitive domains among community-dwelling older adults without dementia.

METHODS: Data were drawn from 5566 community-dwelling older adults without dementia who participated in the National Health and Aging Trends Study from 2015 to 2022. Technology activities assessed included online shopping, banking, medication refills, social media use, and checking health conditions online. The cognitive domains measured were episodic memory, executive function, and orientation. Asymmetric effects models were used to analyze the associations between technology activity transitions and cognitive outcomes, adjusting for demographic, socioeconomic, and health-related covariates. Lagged models were applied for sensitivity analysis.

RESULTS: In the asymmetric effects models, the onset of online shopping (β=.046, P=.02), medication refills (β=.073, P<.001), and social media use (β=.065, P=.01) was associated with improved episodic memory. The cessation of online shopping was associated with faster episodic memory decline (β=-.023, P=.047). In contrast, the cessation of online banking (β=-.078, P=.01) and social media use (β=-.066, P=.003) was associated with decreased episodic memory. The initiation of instrumental, social, and health-related technology activities was associated with slower cognitive decline in orientation. The lagged models further emphasized the effects of stopping online banking and starting online medication refills in relation to episodic memory, as well as the positive associations between online shopping and social media use and orientation. All significant effects were of small magnitude.

CONCLUSIONS: Combining findings from the main and sensitivity analyses, results suggest that interventions designed to support episodic memory in older adults should emphasize promoting the use of online medication refill services and sustaining engagement with online banking, particularly among those who have already established these habits. To support orientation, strategies should focus on facilitating adoption of online shopping and social media use, helping older adults become comfortable navigating these platforms. Future trials are needed to assess the clinical relevance of targeted interventions for specific cognitive domains, to promote the initiation and maintenance of digital activities to help mitigate domain-specific cognitive decline in aging populations.

PMID:41289578 | DOI:10.2196/77227

Neurol Res. 2025 Nov 25:1-14. doi: 10.1080/01616412.2025.2592911. Online ahead of print.

ABSTRACT

OBJECTIVE: To develop a predischarge model for predicting 1-year post-discharge all-cause mortality in patients with aneurysmal subarachnoid haemorrhage (aSAH) following endovascular treatment (EVT).

METHODS: We retrospectively analysed 947 patients with aSAH who were discharged alive between April 2021 and April 2023 from four neurointerventional centres in China as the training cohort. Candidate variables were selected using the least absolute shrinkage and selection operator (LASSO) combined with the Boruta algorithm. Based on these features, six models – logistic regression (LR), XGBoost, random forest (RF), AdaBoost, decision tree, and gradient boosting decision tree (GBDT) – were developed and compared. The optimal model was selected by the area under the receiver operating characteristic curve (AUC). The external validation cohort comprised 692 aSAH patients discharged alive between April 2023 and April 2024 from two additional centres. Model performance was evaluated using AUC, calibration curves, and decision curve analysis (DCA). Given the imbalanced outcome distribution, we applied the Synthetic Minority Over-sampling Technique (SMOTE) to further assess model generalisability.

RESULTS: Among 1,639 patients alive at discharge, 67 (4.1%) died within 1 year. LASSO and Boruta jointly identified five key predictors for model construction: age, modified World Federation of Neurosurgical Societies (mWFNS) grade, ICU length of stay (ICU-LOS), C-reactive protein (CRP), and monocyte-to-HDL ratio (MHR). The random forest achieved the best discrimination in training set and remained strong in external validation cohorts.Moreover, SMOTE training yielded further improvements in generalisability.

CONCLUSION: Random forest model enables individualised pre-discharge risk stratification and may guide perioperative management.

PMID:41289577 | DOI:10.1080/01616412.2025.2592911

J Med Internet Res. 2025 Nov 25;27:e73131. doi: 10.2196/73131.

ABSTRACT

BACKGROUND: Preventive strategies integrated with digital health and artificial intelligence (AI) have significant potential to mitigate the global burden of atherosclerotic cardiovascular disease (ASCVD). AI-enabled clinical decision support (CDS) systems increasingly provide patient-specific insights beyond traditional risk factors. Despite these advances, their capacity to enhance clinical decision-making in resource-constrained settings remains largely unexplored.

OBJECTIVE: We conducted a randomized controlled study to assess the effect of AI-based CDS on 10-year ASCVD risk assessment and management in primary prevention.

METHODS: In a 3-way, within-subject randomized design, doctors completed 9 clinical vignettes representative of primary care presentations in a resource-constrained outpatient setting. For each vignette, participants assessed 10-year ASCVD risk and made management decisions using a conceptual prototype of AI-based CDS, automated CDS, or no decision support. The conceptual prototype represented contemporary risk calculators based on traditional machine learning models (eg, random forest, neural networks, logistic regression) that incorporate additional predictors alongside traditional risk factors. Primary outcomes were correct risk assessment and patient management (prescription of aspirin, statins, and antihypertensives; referral for advanced examinations). Decision-making time and perceptions about AI utility were also measured.

RESULTS: In total, 102 doctors from all 7 geographical regions of Indonesia participated. Most (n=85, 83%) participants were 26-35 years of age, and 57 (56%) were male, with a median of 6 (IQR 4.75) years of clinical experience. AI-based CDS improved risk assessment by 27% (χ22 (n=102)=48.875, P<.001) when compared to unassisted risk assessment, equating to 1 additional correct risk classification for every 3.7 patients where doctors used AI (number needed to treat=3.7, 95% CI 2.9-5.2). The prescription of statins also improved by 29% (χ22 (n=102)=36.608, P<.001). In pairwise comparisons, doctors who were assisted by the AI-based CDS correctly assessed significantly more cases (z=-5.602, n=102, adjusted P<.001) and prescribed the appropriate statin more often (z=-4.936, adjusted P<.001, medium effect size r=0.35) when compared with the control. AI-assisted cases required less time (estimated marginal means 63.6 s vs 72.8 s, F2, 772.8=5.710, P=.003). However, improvements in the prescription of aspirin and antihypertensives did not reach statistical significance (P=.08 and P=.30, respectively). No improvement was observed in referral decisions. Participants generally viewed AI-based CDS positively, with 81 (79%) agreeing or strongly agreeing that they would follow its recommendations and 82 (82%) indicating they would use it if given access. They believed CDS could enhance the efficiency of risk assessment, particularly in high-volume primary care settings, while noting the need to verify AI recommendations against clinical guidelines for each patient.

CONCLUSIONS: Improvements in risk assessment and statin prescription, coupled with reduced decision-making time, highlight the potential utility of AI in ASCVD risk assessment, particularly in resource-constrained settings where efficient use of health care resources and doctors’ time is crucial. Further research is needed to ascertain whether improvements observed in this online study translate to real-world low-resource settings.

PMID:41289576 | DOI:10.2196/73131

J Med Internet Res. 2025 Nov 25;27:e72274. doi: 10.2196/72274.

ABSTRACT

BACKGROUND: The multidisciplinary life course theory emphasizes the relation between a person’s choices and their socioeconomic context, and their capacity to make decisions within existing opportunities or constraints. Older age is particularly characterized by social and environmental conditions that may impact people’s use of technology and eHealth applications.

OBJECTIVE: This research aims to present an overview of eHealth application use among older Canadian adults and examine the relationship between eHealth use and social and health system interaction determinants.

METHODS: We conducted a national cross-sectional survey of older adults (n=2000) in Canada, assessing their technology (eg, tablets, computers) and eHealth application (eg, fall detection and telemonitoring technologies, internet) use, social determinants (eg, sociodemographic characteristics, environmental living conditions), and health system interactions (eg, health status, access to care, services utilization).

RESULTS: There is technological readiness (owned a computer: 1703/2000, 85.2%; used the internet daily or a few times per week: 1652/2000, 82.6%) among older Canadian adults, although it does not translate into eHealth application use. Internet use to connect with health care professionals, access results or patient portals, or book medical appointments was limited. The use of telemonitoring and fall detection technologies was low (189/2000, 9.4%, and 84/2000, 4.2%, respectively). There were significant variations in eHealth use, highlighting the importance of accounting for social determinants and interactions with the health care system. Of the variance in online access to laboratory results, 12.7% was explained by the province of residence (higher in Ontario and British Columbia), living environment (lower in rural settings), and access or need variables (higher for those with private insurance and willingness to pay for quicker access; higher for those hospitalized). Women reported more internet use for self-diagnosis and looking for online information. Individuals with excellent perceived health and those with no recent emergency visits or home care services reported greater use of mobile health apps and fall detection technologies (odds ratio [OR]=2.16, 95% CI 1.23-3.80; OR=3.427, 95% CI 1.55-7.60), respectively. A digital divide exists within the older adult population, which raises concerns about whether those with higher needs and limited resources have access to and can benefit from eHealth applications.

CONCLUSIONS: Addressing the digital health gap among older adults is not simply a matter of technological access but also a matter of health equity and system sustainability. Without deliberate policies, digital health risks reinforcing existing disparities by disproportionately excluding those with the greatest health needs and the fewest resources. Our findings identify the groups most at risk of digital exclusion, such as rural residents, institutionalized older adults, and those with limited financial or insurance coverage, and point to where interventions can yield the greatest benefit.

PMID:41289575 | DOI:10.2196/72274

JMIR Aging. 2025 Nov 25;8:e80700. doi: 10.2196/80700.

ABSTRACT

BACKGROUND: Digital inclusion has become increasingly important in promoting healthy aging, yet its association with mental health among older adults appears complex and heterogeneous. The role of cognitive function as a moderator and the underlying mechanisms remain understudied.

OBJECTIVE: This study aims to examine cognitive function’s moderating role in the relationship between digital inclusion and depression risk among older adults, and to investigate multiple pathways of association.

METHODS: Using data from the 2020 wave of the China Health and Retirement Longitudinal Study, we analyzed 18,673 adults aged 60 years and above (mean age 68.4 y, SD 6.5; 50.8% male participants [n=9486], 49.2% female participants [n=9187]). We constructed interaction effect models to test the moderation hypothesis and employed path analysis with bootstrapped 95% confidence intervals (2000 iterations) to investigate multiple pathways through which digital inclusion is associated with depression.

RESULTS: Cognitive function significantly moderated the digital inclusion-depression relationship (β=-.002, P=.03). The association was not statistically significant at low cognitive function (β=-.137, P=.33) but strongly protective at high cognitive function (β=-.517, P<.001), revealing a “cognitive threshold effect.” Path analysis identified 3 significant pathways: direct effects (66.7% of total effect), cognitive enhancement (8.3%), and social participation (8%). Importantly, higher digital inclusion was not found to be associated with increased depression risk at any cognitive function level.

CONCLUSIONS: Our findings suggest that older adults require adequate cognitive resources to derive mental health benefits from digital participation, though no harmful effects were observed at lower cognitive levels. This asymmetric pattern has important implications for designing cognitive-informed digital inclusion programs that integrate digital skills training with cognitive enhancement strategies for promoting mental health in aging populations.

PMID:41289574 | DOI:10.2196/80700

JMIR Ment Health. 2025 Nov 25;12:e77796. doi: 10.2196/77796.

ABSTRACT

BACKGROUND: The impact of smartphone use on sleep remains intensely debated. Most existing studies have used self-reported smartphone use data. Moreover, few studies have simultaneously examined associations between both smartphone addiction and objectively measured smartphone use and sleep, and the dose-response relationship between smartphone use and risk of poor sleep has been consistently overlooked, requiring systematic and further research on this topic.

OBJECTIVE: This study aimed to examine the associations between smartphone addiction and objectively measured smartphone use and sleep quality and duration.

METHODS: This cross-sectional study enrolled 17,713 participants from a university in China. We assessed objective smartphone screen time and unlocks by collecting screenshots of use records and measured smartphone addiction using a validated questionnaire. Sleep quality and duration were estimated via the Pittsburgh Sleep Quality Index. Binary logistic regression, linear regression, and restricted cubic spline regression models were used for the analyses.

RESULTS: A total of 14.3% (2533/17,713) of the participants met the criterion for poor sleep, with a mean sleep duration of 507.1 (SD 103.2) minutes per night. Notably, university students with smartphone addiction exhibited 184% higher risk of poor sleep (odds ratio [OR] 2.84, 95% CI 2.59-3.11) and a 15.47-minute-shorter nighttime sleep duration (β=-15.47, 95% CI -18.53 to -12.42) compared to those without smartphone addiction. Regarding objectively measured smartphone use, participants with ≥63 hours per week of smartphone screen time had 22% higher odds of poor sleep (OR 1.22, 95% CI 1.08-1.37) and a 6.66-minute-shorter nighttime sleep duration (β=-6.66, 95% CI -10.19 to -3.13) compared to those with 0 to 21 hours of screen time per week, whereas those with approximately 21 to 42 hours per week of smartphone screen time had a 5.47-minute-longer nighttime sleep duration (β=5.47, 95% CI 1.28-9.65). Similarly, compared to those with 0 to 50 smartphone unlocks per week, participants with ≥400 smartphone unlocks per week showed 61% higher odds of poor sleep (OR 1.61, 95% CI 1.41-1.85) accompanied by a 4.09-minute-shorter nighttime sleep duration (β=-4.09, 95% CI -8.08 to -0.09), whereas those with approximately 50 to 150 smartphone unlocks per week had a 5.84-minute-longer sleep duration (β=5.84, 95% CI 2.32-9.36). An inverted U-shaped association between smartphone screen time and sleep duration was observed (P<.001 for nonlinearity).

CONCLUSIONS: Smartphone addiction, excessive objectively measured smartphone screen time, and unlocks are positively associated with both sleep quality and duration. Restricted cubic spline analyses revealed different nuanced dose-response relationships, with an inverted U-shaped association observed between smartphone screen time and sleep duration.

PMID:41289567 | DOI:10.2196/77796

JMIR Form Res. 2025 Nov 25;9:e76896. doi: 10.2196/76896.

ABSTRACT

BACKGROUND: Large language models (LLMs) have great potential to improve and make the work of clinicians more efficient. Previous studies have mainly focused on web-based services, such as ChatGPT, often with simulated cases. For the processing of personalized patient data, web-based services have major data protection concerns. Ensuring compliance with data protection and medical device regulations therefore remains a critical challenge for adopting LLMs in clinical settings.

OBJECTIVE: This retrospective single-center study aimed to evaluate locally run LLMs (Gemma 2, Mistral Nemo, and Llama 3) in providing diagnosis and treatment recommendation for real-world outpatient cases in otorhinolaryngology (ORL).

METHODS: Outpatient cases (n=30) from regular consultation hours and the emergency service at a university hospital ORL outpatient department were randomly selected. Documentation by ORL doctors, including anamnesis and examination results, was passed to the locally run LLMs (Gemma 2, Mistral Nemo, and Llama 3), which were asked to provide diagnostic and treatment strategies. Recommendations of the LLMs and the treating ORL doctors were rated by 3 experienced ORL consultants on a 6-point Likert scale for medical adequacy, conciseness, coherence, and comprehensibility. Moreover, consultants were asked whether the answers pose a risk to the patient’s safety. A modified Turing test was performed to distinguish responses generated by LLMs from those of doctors. Finally, the potential influence of the information generated by the LLMs on the raters’ own diagnosis and treatment opinions was evaluated.

RESULTS: Over all categories, ORL doctors achieved superior (P<.0005) ratings compared to locally run LLMs (Llama 3, Mistral Nemo, and Gemma 2). ORL doctors’ responses were considered hazardous for patients in only 1% of the ratings, whereas recommendations by Llama 3, Gemma 2, and Mistral Nemo were considered hazardous in 54%, 47%, and 32% of cases, respectively. According to the raters, the LLM’s information rarely influenced their judgment, with Mistral Nemo, Gemma 2, and Llama 3 achieving 1%, 3%, and 4% of the ratings, respectively.

CONCLUSIONS: Although locally run LLM models still underperform compared with their web-based counterparts, they achieved respectable results on outpatient treatment in this study. Nevertheless, the retrospective and single-center nature of the study, along with the clinicians’ documentation style, may have introduced bias in favor of human recommendations. In the future, locally run LLMs will help address data protection concerns; however, further refinement and prospective validation are still needed to meet strict medical device requirements. As locally run LLMs continue to evolve, they are likely to become comparably powerful to web-based LLMs and become established as useful tools to support doctors in clinical practice.

PMID:41289564 | DOI:10.2196/76896

JCO Oncol Pract. 2025 Nov 25:OP2500584. doi: 10.1200/OP-25-00584. Online ahead of print.

ABSTRACT

PURPOSE: Demand for integrative medicine (IM) in cancer care is increasing, yet limited data exist regarding which hospitals offer IM services and what those services include. This exploratory cross-sectional study aimed to (1) examine organizational characteristics and market factors of cancer hospitals that offered IM services and those that did not; and (2) identify the types of services included in IM programs within cancer hospitals and their funding sources.

METHODS: Bivariate analysis evaluated the relationship between the dependent variable (cancer hospitals offering IM services) and the independent categorical variables. χ² tests were performed for categorical variables, and t tests were used to compare means for continuous variables. An electronic survey was developed and distributed to 150 IM program leaders. The survey assessed service offerings, delivery settings, referrals, funding, barriers, and facilitators. Descriptive statistics were used to summarize and describe survey results.

RESULTS: Key findings showed statistically significant associations between cancer hospitals providing IM services and those that did not. A higher proportion of cancer hospitals with IM programs offered palliative care services, were not-for-profit, larger (bed size 400+), teaching entities located in metropolitan areas with larger populations and higher per capita income. Most frequently offered IM services were nutrition counseling, acupuncture, and massage therapy. Additionally, philanthropy was essential in providing financial support for IM services.

CONCLUSION: This study emphasizes the need for a better understanding of the factors influencing the availability and accessibility of IM services in cancer care. IM programs are increasingly embedded within cancer centers, offering diverse services to support patients’ symptom management and quality of life. However, funding, staffing, and integration variations reflect a need for standardized models and best practices.

PMID:41289555 | DOI:10.1200/OP-25-00584