Category: Statistics

J Am Acad Orthop Surg Glob Res Rev. 2026 Mar 17;10(3). doi: 10.5435/JAAOSGlobal-D-26-00001. eCollection 2026 Mar 1.

ABSTRACT

INTRODUCTION: MRI is commonly used to evaluate pelvic musculoskeletal infections. Limited “quick” MRI protocols enable timely imaging without intravenous contrast or sedation. This study examines the consistency of interpretation among pediatric orthopaedic surgeons when using quick versus full, contrast-enhanced MRI scans in cases of peripelvic musculoskeletal infection and explores these findings to inform future development and design of machine learning algorithms.

METHODS: Twenty-nine pediatric patients with full pelvis MRI with and without contrast and culture-positive peripelvic infection were retrospectively identified. Two deidentified files were created for each patient: one including all sequences and the other containing only the limited sequences included in our institution’s quick MRI protocol. Three pediatric orthopaedic surgeons independently and sequentially evaluated the images, followed by group discussion to reach consensus on the primary diagnosis and management. Fleiss’ Kappa (FK) statistic was calculated for each outcome.

RESULTS: Moderate agreement in primary diagnosis was observed among reviewers using quick MRI sequences (Kappa = 0.488), and substantial agreement was seen with full sequences (0.684; P = 0.003). Inter-rater agreement on treatment recommendations was poor with both quick (0.09) and full (0.233) MRI (P = 0.046). No difference was found in team consensus diagnosis and final diagnosis between quick (0.523) and full (0.569) MRI (P = 0.662). Poor agreement was found between team treatment recommendations and actual treatment for both quick (0.182) and full (0.07) MRI (P = 0.254).

CONCLUSION: Independent evaluation of limited, quick MRI sequences by pediatric orthopaedic surgeons showed more variability in diagnosis and treatment compared with full MRI review. When reviewed collaboratively, the diagnostic accuracy of quick MRI approached that of full MRI. Future artificial intelligence-based imaging interpretation platforms will benefit from multi-institutional collaboration to improve training data quality; use of ensemble learning techniques to reflect the diversity of multispecialist approaches; and incorporation of relevant clinical data to properly identify, triage, and direct treatment of complex pediatric musculoskeletal conditions.

PMID:41843807 | DOI:10.5435/JAAOSGlobal-D-26-00001

JMIR Form Res. 2026 Mar 17;10:e59700. doi: 10.2196/59700.

ABSTRACT

BACKGROUND: The prevalence of various mental health conditions is higher among physicians and medical learners. One common barrier to receiving adequate care includes a lack of time to see a provider and follow treatment plans. As such, virtual forms of cognitive behaviour therapy with mindfulness (CBTm) were introduced to mitigate these barriers and provide care in an efficient and effective manner.

OBJECTIVE: The objective of this study was to determine the acceptability and feasibility of a 5-session CBTm program, delivered in 2 virtual formats within a population of medical learners and physicians.

METHODS: Participants signed up to the program using an online link and were able to choose a preferred format to participate in the CBTm program. One option was a virtual, facilitator-led class that was held once a week for 5 weeks, in a group setting (CBTm facilitator-led). Another option included a self-directed course that had identical content to the live classes but was independently completed by the participant using an online platform (CBTm self-directed). Feedback forms were collected from participants after every class and analyzed using quantitative and qualitative methods. Thematic analysis was used to qualitatively analyze open-ended questions from participant feedback forms. In addition, the mean values of questionnaire items were used to determine participant satisfaction with the program.

RESULTS: The results indicated a good level of interest in both CBTm facilitator-led (n=15) and CBTm self-directed (n=94) groups. Of those who registered for the program, 13.8% (15/109) registered for CBTm facilitator-led and 86.2% (94/109) chose the self-directed version. The percentage of participants who participated in the majority of classes was 80% (12/15) for the CBTm facilitator-led group and 45.7% (43/94) for the CBTm self-directed group. The mean age of participants was 44.86 (SD 12.15 years), and the highest rate of uptake was among female physicians. Quantitative mean scores of participant feedback forms also showed a high level of satisfaction. For example, the Client Satisfaction Questionnaire 8 (CSQ-8) was analyzed, and the results indicated mean total scores of 28.00 (SD 3.24) and 26.46 (SD 3.55) for CBTm facilitator-led and CBTm self-directed, respectively. In addition, many themes emerged from thematic analysis and were subsequently categorized into 3 major categories. This included perceived strengths, perceived weaknesses, and suggested revisions to improve the program. Perceived strengths included improved mental health, helpful course content, and improved patient care. Perceived weaknesses included individual barriers to participation, content downfalls, and format-specific barriers. Suggested revisions included improving adherence to homework and virtual delivery of the program.

CONCLUSIONS: In conclusion, the results indicate that the self-directed and facilitator-led versions of CBTm were acceptable and feasible in this population of physicians and medical learners.

PMID:41843800 | DOI:10.2196/59700

Cutan Ocul Toxicol. 2026 Mar 17:1-8. doi: 10.1080/15569527.2026.2642191. Online ahead of print.

ABSTRACT

CLINICAL RELEVANCE: Cadmium causes systemic inflammation and retinal damage, posing a serious threat to visual and public health.

BACKGROUND: Cadmium is a harmful heavy metal that builds up in body tissues and cause systemic inflammation and organ damage. This research sought to explore the impact of subacute cadmium exposure on retinal morphology and inflammatory cytokine levels.

METHOD: A total of fourteen male Wistar albino rats were randomized into control and cadmium-exposed groups within the scope of the experiment. 3 mg/kg cadmium (CdCl₂) was administered intraperitoneally to the subjects in the cadmium group for 15 days. At the end of study, serum IL-6 and TNF-α levels were determined by ELISA method. In addition, retinal tissues were examined histopathologically after Hematoxylin and Eosin staining. Retinal apoptotic changes were assessed by semi-quantitative immunohistochemical analysis of Bax and Bcl-2 expression in the inner and outer nuclear layers.

RESULTS: According to the findings, cadmium exposure caused a statistically significant increase in serum IL-6 and TNF-α levels. Histopathological examination revealed a marked decrease in the thickness of the inner plexiform layer (IPL), inner nuclear layer (INL), and outer nuclear layer (ONL), as well as focal vacuolization and cellular disorganization. Cadmium exposure significantly increased Bax immunoreactivity and the Bax/Bcl-2 ratio in both the INL and ONL, while significantly decreasing Bcl-2 expression.

CONCLUSION: In conclusion, cadmium exposure increased the systemic inflammatory response, leading to significant histopathological damage in the retinal layers and a dominant proapoptotic Bax/Bcl-2 balance, thereby triggering cellular apoptosis.

PMID:41843795 | DOI:10.1080/15569527.2026.2642191

JMIR Form Res. 2026 Mar 17;10:e63135. doi: 10.2196/63135.

ABSTRACT

BACKGROUND: Electronic medical records (EMRs) are increasingly adopted globally to improve health care delivery, yet challenges remain in their acceptance, defined here as favorable attitudes toward their use among health professionals. Understanding factors influencing acceptance is critical for successful implementation.

OBJECTIVE: This study aimed to identify predictors (or factors) associated with favorable attitudes toward EMRs among health professionals in 3 Ethiopian hospitals.

METHODS: A cross-sectional study was conducted from January to March 2025 in 3 Ethiopian hospitals implementing EMRs. A systematic random sampling method was used to initially select 397 health professionals, and data were collected using a structured questionnaire. Multivariate logistic regression was employed to identify predictors of favorable attitudes toward EMRs.

RESULTS: Of the final 382 professionals, 198 (51.8%, 95% CI 0.43-0.53) showed favorable attitudes. Predictors of positive attitude included computer literacy (adjusted odds ratio [AOR] 2.66, 95% CI 1.16-6.09; P=.02), EMR training (AOR 2.87, 95% CI 1.80-4.56; P<.001), and age of 29 years or younger (AOR 3.05, 95% CI 1.58-5.9; P=.001).

CONCLUSIONS: Improving computer literacy, providing refresher training, and strengthening management support are key strategies for enhancing health professionals’ attitudes toward EMRs. Future research should explore qualitative insights into barriers and facilitators of EMR adoption.

PMID:41843793 | DOI:10.2196/63135

JMIR Mhealth Uhealth. 2026 Mar 17;14:e75662. doi: 10.2196/75662.

ABSTRACT

BACKGROUND: Regular physical activity is a crucial and an important modifiable lifestyle factor reducing the risk of recurrent incidents after stroke or transient ischemic attack (TIA). Mobile health (mHealth) has emerged as a promising approach for providing long-term support for physical activity. However, little is known about how individuals poststroke or TIA adhere to and engage with mHealth interventions.

OBJECTIVE: This study aimed to (1) describe adherence to supervised sessions in an mHealth intervention targeting physical activity, (2) describe engagement with self-managed mHealth support for physical activity during and after the intervention, (3) compare characteristics of participants with high and low adherence and app engagement, and (4) examine whether high adherence and app engagement were associated with maintained physical activity after having completed the intervention and at a 12-month follow-up.

METHODS: In this study, a secondary analysis of data from the experimental arm of a feasibility randomized controlled trial was conducted. The experimental group received a 6-month mHealth version of the i-REBOUND intervention, which included supervised mHealth support for physical activity and behavior change, followed by a 6-month postintervention period with access to self-managed mHealth support. The control group received mHealth consultations via video conferencing. Adherence measures included attendance at supervised exercise and counseling sessions, while app engagement was measured by weekly interactions with self-managed mHealth support during and after the intervention. Participants’ level of physical activity (steps per day) was measured using accelerometers at baseline, and at 6- and 12-month postbaseline. Logistic regression analysis examined the associations between high adherence and app engagement during the intervention and postintervention period and maintained physical activity (ie, >7000 steps/day) across the 12-month study period.

RESULTS: Of the 57 participants enrolled, 51 (89%) completed the intervention; the average age was 71 years, 34/51 (67%) were female, and 47/51 (92%) had mild stroke symptoms. Adherence to supervised mHealth support was high (supervised exercise sessions: 79%, counseling sessions: 98%), while engagement with self-managed mHealth support was high during the intervention (83%) but declined postintervention (38%). A larger proportion of females (24/31, 77%) demonstrated high adherence to the intervention compared to males (7/31, 23%, χ²1=4.1; P=.04). High adherence (≥80%) during the intervention was associated with maintained physical activity between baseline and the 6-month follow-up (OR 12.07, 95% CI 2-72.76; P=.01), while high app engagement (≥80%) during postintervention was associated with maintained physical activity between the 6- and 12-month follow-up (OR 5.10, 95% CI 1.02-25.52; P=.05).

CONCLUSIONS: Supervised mHealth support was well received with high adherence, while modules for self-management of physical activity faced challenges in engaging the participants. Future studies could benefit from qualitative and cocreative approaches to better understand and refine self-managed mHealth support for individuals poststroke or TIA.

PMID:41843778 | DOI:10.2196/75662

JMIR Form Res. 2026 Mar 17;10:e76018. doi: 10.2196/76018.

ABSTRACT

BACKGROUND: Using social media platforms has been demonstrated to be a successful recruitment method, especially for young people. Two cited benefits of using social media for recruitment are its ability to quickly increase sample size and engage hard-to-reach participants.

OBJECTIVE: This study aimed to (1) provide a pragmatic depiction of co-designing and implementing a social media strategy with our advisory group and (2) compare demographic information of participants recruited via social media and other methods. Our objective was to provide evidence for future trials to implement social media recruitment with maximum efficiency.

METHODS: Participants were 2369 UK university students who consented to take part during the recruitment timeframe of 2 mental health trials. Our student advisory group advised on content, platform, and timing of engagement. We trialed 10 different adverts over a 12-month recruitment period. Descriptive analysis evaluated advert reach and link clicks using Meta/TikTok business data, website traffic using Google Analytics, screening consent, and enrollment using REDCap (Research Electronic Data Capture; Vanderbilt University) software. A cross-sectional observational analysis used chi-square and t tests to compare ethnicity, gender, sexual orientation, disability status, and university attended among 842 participants recruited via social media and 1527 participants recruited by other methods.

RESULTS: Through extensive student advisor input, an Instagram carousel advert led to a boost in participant recruitment. However, this fluctuated over the academic year, with numbers dropping completely over the summer months. All tests used α=.05. There was a difference in gender among those recruited through social media versus other recruitment methods on campus (χ²2=8.34, P=.02), with social media resulting in a higher proportion of gender-diverse students (27/370, 7% vs 30/711, 4%; 95% CI 3.4%-13.7%), but fewer male students (35/370, 4%, 95% CI [3.4%-13.7%] vs 99/711, 7%, 95% CI [1.6%-9.8%]). Those recruited from social media were younger than those recruited through other methods, with a mean difference of -3.49 years (SE=0.31, 95% CI [-3.94 to -3.04]; t1927.5=15.146, P<.001). A significantly higher proportion of students in the social media sample were from the LGBTQIA+ community (180/351, 51%, 95% CI [41.3%-60.6%] vs 350/711, 37%, 95% CI [28.2%-46.8%]; χ²1=17.87, P<.001). There was also a significant difference in the reported disability (103/375, 27.5%, 95% CI [19.7%-37.0%] vs 154/723, 21.3%, 95% CI [14.4%-30.3%]; χ²1=4.90, P=.03). There was no difference in ethnicity between the 2 groups (χ²1=2.4609, P=.12).

CONCLUSIONS: Our study describes how different recruitment approaches influence participant characteristics in clinical trials and highlights challenges in implementing a co-designed recruitment strategy in a university setting. This contributes to the field by providing research evidence on the efficacy of different recruitment strategies for planning future trials. Our key real-world recommendation is to allow time and resources for planning multiple recruitment strategies to ensure a diverse range of participants take part in research.

PMID:41843777 | DOI:10.2196/76018

JMIR Aging. 2026 Mar 17;9:e83078. doi: 10.2196/83078.

ABSTRACT

BACKGROUND: Rapid population aging and the intensifying digitalization of everyday life are unfolding simultaneously in China. While prior studies have largely examined pairwise associations among digital inclusion, social engagement, mental health, and overall health status, few have evaluated an integrated, theoretically grounded pathway linking these domains in later life.

OBJECTIVE: This study aims to quantify the direct and indirect pathways through which digital inclusion influences older adults’ overall health status, social engagement, and mental health, specified as sequential mediators.

METHODS: We analyzed the newly released, nationally representative data from the 2023 wave of the China Longitudinal Aging Social Survey, comprising 9918 adults aged 60 years or older. Overall health status was assessed using 3 self-rated health (SRH) indicators: current SRH, SRH relative to age peers, and SRH relative to last year. Digital inclusion was measured through digital access, device proficiency, and digital ability. Social engagement captured social support, frequency of participation in community or voluntary activities, and nononline activities. Mental health included depressive symptoms, social adaptation, and life satisfaction. Analyses included descriptive statistics, multivariable hierarchical linear regressions, and structural equation modeling to estimate direct and mediated effects (2-sided; α=.05).

RESULTS: Older age, chronic disease, and functional limitations were associated with poorer overall health status, whereas higher education and current employment were associated with better health status. Digital inclusion was positively associated with social engagement (β=.50), which in turn was positively associated with mental health. Mental health showed the strongest association with SRH (β=.74). The direct path from social engagement to overall health status was nonsignificant (P=.34), indicating that participation influences health primarily through psychological pathways. In regression analyses, digital inclusion modestly improved model fit for health status outcomes, while adding mental health produced a greater increase.

CONCLUSIONS: Digital inclusion promoted active aging indirectly, by expanding social engagement and enhancing mental health, thereby improving overall health status. Policy efforts should prioritize narrowing the digital divide by improving digital skills and capability, rather than access alone. Meaningful opportunities for social engagement should also be expanded to strengthen community-based mental health support. In addition, strategies should be tailored to the differing needs of urban and rural settings.

PMID:41843772 | DOI:10.2196/83078

Comput Inform Nurs. 2026 Mar 16. doi: 10.1097/CIN.0000000000001510. Online ahead of print.

ABSTRACT

Medication dosage calculation is a vital clinical skill essential for patient safety. However, many nursing students experience difficulties mastering this competency due to mathematical anxiety and limited practice. Artificial intelligence (AI)-based tools, such as ChatGPT, offer interactive and learner-centered educational experiences by providing personalized guidance and immediate feedback. This study aimed to evaluate the effects of ChatGPT-assisted medication dosage calculation training on nursing students’ knowledge, calculation accuracy, test completion time, and learning satisfaction. A single-group quasi-experimental pretest-post-test design was implemented in the nursing department of a public university in Turkey. A total of 41 first-year nursing students voluntarily participated. A 4-session ChatGPT-assisted training program was delivered, focusing on unit conversions, dilution, pediatric dosages, and infusion rate calculations. Data were collected through a medication knowledge test, stopwatch-measured test completion time, a Visual Analog Scale for satisfaction, and open-ended feedback. Data were analyzed using descriptive statistics, paired t tests, and Cohen’s d post-training knowledge scores increased from 49.85±21.83 to 77.36±19.19 (P<.001, d=1.26). Unanswered questions decreased from 7.95±5.30 to 1.17±2.08 (P<.001, d=1.28). Time per question decreased from 2.02±2.95 to 0.90±1.95 minutes (P=.019, d=0.38). The satisfaction score was 9.02±0.99. Most students (95.1%) preferred the AI-assisted method over traditional training. ChatGPT-assisted training significantly enhanced nursing students’ knowledge, accuracy, test efficiency, and satisfaction. These results support integrating AI tools into nursing education to improve clinical competence and engagement.

PMID:41843767 | DOI:10.1097/CIN.0000000000001510

JMIR Med Educ. 2026 Mar 17;12:e79134. doi: 10.2196/79134.

ABSTRACT

BACKGROUND: The exponential growth of medical knowledge presents a paradox for modern medical education. While access to information is immediate, applying it in a clinically meaningful way remains a challenge. Large language models (LLMs), such as ChatGPT, are widely used for information retrieval, yet their role in dynamic, high-pressure clinical learning remains poorly understood.

OBJECTIVE: This study aims to evaluate whether unstructured access to an LLM improves decision-making, teamwork, and confidence in trauma education for medical students.

METHODS: This randomized controlled pilot study involved 41 final-year medical students participating in a trauma simulation session. Students self-selected into teams of 4 to 6 and were randomized to either an LLM-assisted group (ChatGPT-4o mini) or a control group without LLM access. All teams completed 18 video-based trauma scenarios requiring time-sensitive clinical decisions. Prompting was unrestricted. Confidence and trauma exposure were assessed using pre- or postquestionnaires. Facilitators rated teamwork (1-5), decision accuracy, and response times. Knowledge retention was measured 4 weeks later via an online quiz.

RESULTS: Confidence in trauma management improved in both groups (P<.001), with larger gains in the non-LLM group (P=.02). LLM support did not enhance the decision accuracy or speed and was associated with longer response times in some complex cases. Teams without LLMs demonstrated more active discussion and scored higher in teamwork ratings (median 5.0 [IQR 5.0-5.0] vs median 3.5 [IQR 3.0-4.5]; P=.08). Students primarily used the LLM for fact-checking but reported vague or overly general responses. Knowledge retention was high across both groups and did not differ significantly (P=.33).

CONCLUSIONS: While students appreciated the inclusion of artificial intelligence (AI), unstructured LLM use did not improve performance and may have disrupted the group reasoning. The use of non-English prompting likely contributed to lower AI performance, underscoring the importance of language alignment in LLM applications. This pilot study highlights the need for structured AI integration and targeted instruction in AI literacy. Simulation-based trauma education proved effective and well received, but optimizing the educational value of LLMs will require thoughtful curricular design. Further studies with more students are needed to define best practices for LLM use in clinical education.

PMID:41843765 | DOI:10.2196/79134

J Med Internet Res. 2026 Mar 17;28:e80529. doi: 10.2196/80529.

ABSTRACT

BACKGROUND: Unplanned emergency department (ED) visits and hospital readmissions following discharge contribute to patient distress, increased health care costs, and system inefficiencies. Early postdischarge follow-up can improve care transitions, yet evidence on the effectiveness of telephone-based interventions remains mixed. Telephone calls, a low-barrier form of digital health, may enhance equity and accessibility by reaching patients who face challenges with in-person or higher-technology follow-up.

OBJECTIVE: This study evaluated the impact of a nurse-led postdischarge telephone intervention delivered by Fraser Health Virtual Care on short-term ED visits and hospital readmissions among recently discharged high-risk patients. Secondary objectives included examining patient experiences with the service and identifying care gaps addressed during follow-up calls.

METHODS: A pragmatic quasi-randomized trial was conducted (May 2022-September 2022). Participants were eligible if they were aged 18 years or older and classified as high-risk for readmission using the LACE (Length of stay, Acuity of admission, Comorbidities, and Emergency department use) index (≥10 or <9 and ≥45 y). Participants were allocated to either a postdischarge telephone intervention group or a standard care control group based on daily nurse availability. Intervention participants received a structured nurse-led call 48 hours after discharge assessing understanding of discharge instructions, medication management, follow-up appointments, and home supports. Primary outcomes were ED visits within 7 and 30 days post call; secondary outcomes were hospital readmissions and patient experience. Negative binomial regression models were used to calculate adjusted incident rate ratios (IRRs).

RESULTS: A total of 7091 participants were included (intervention: n=3911, of whom 1752 completed the call; control: n=3180). Postdischarge calls significantly reduced ED visits at both 7 days (adjusted IRR 0.719, 95% CI 0.617-0.837; P<.001) and 30 days (IRR 0.878, 95% CI 0.783-0.983; P=.02). No statistically significant reductions were observed in hospital readmissions at either 7 days (IRR 0.809; P=.13) or 30 days (IRR 0.942; P=.54). Forty percent of completed calls (n=701) identified at least 1 gap in discharge understanding or follow-up care. Most participants found the calls helpful and reported increased confidence in managing their care.

CONCLUSIONS: Structured nurse-led postdischarge telephone calls significantly reduced short-term ED utilization but did not impact readmission rates. These findings support the role of telephone-based virtual care as a scalable, low-barrier strategy to improve care transitions and reduce avoidable ED visits. Additional or ongoing interventions may be required to influence hospital readmission outcomes among high-risk patients.

PMID:41843752 | DOI:10.2196/80529