N Engl J Med. 2025 Dec 17. doi: 10.1056/NEJMp2513633. Online ahead of print.
NO ABSTRACT
PMID:41406466 | DOI:10.1056/NEJMp2513633
Category: Nevin Manimala
JMIR Public Health Surveill. 2025 Dec 17;11:e80381. doi: 10.2196/80381.
ABSTRACT
BACKGROUND: Overweight and obesity are major risk factors for numerous chronic diseases, requiring effective prevention and intervention strategies. Community-based BMI screening may enhance awareness of weight status, but its effectiveness remains uncertain.
OBJECTIVE: This study aimed to rigorously evaluate the long-term causal effects of community-based BMI screening with a light-touch intervention in Malaysia using a regression discontinuity design (RDD).
METHODS: Using data from 2 waves (2013 and 2018) of a Malaysian population-based cohort study (N=6561), we applied a sharp RDD to estimate the causal effects of community-based BMI screening on health outcomes for individuals near the BMI threshold. Participants were aged 35 years or older and completed both follow-ups. The exposure was BMI screening with a light-touch intervention, including height and weight measurement, feedback on results, and referral card distribution. Main outcomes were BMI, blood pressure, and random blood glucose 5 years post intervention, along with health behaviors, health care use, and mental health status.
RESULTS: BMI screening and intervention showed no significant impact on BMI after 5 years (0.4 kg/m², 95% CI -0.2 to 0.9, P=.16). Results remained robust after adjusting for covariates (eg, 0.4 kg/m², 95% CI -0.1 to 0.9 with age and sex; 0.5 kg/m², 95% CI -0.1 to 1.0 with demographic covariates) and modifying functional forms (0.4 kg/m², 95% CI -0.2 to 1.1 with quadratic specification). Robustness was also confirmed across different bandwidths, placebo tests, “donut” RDD, and when treating age as either a continuous or categorical variable. Interaction analysis revealed almost no substantial heterogeneity effects. Mechanism analysis and secondary outcomes indicated no significant effects on health behaviors (including smoking, physical activity, diet, and sedentary behavior), health care use (screening, diagnosis, and medication treatment of hypertension and diabetes), mental health outcomes (anxiety, depression, and stress levels), or cardiovascular risk factors (systolic blood pressure, diastolic blood pressure, random blood glucose; eg, systolic blood pressure showed a nonsignificant change of 0.2, 95% CI -3.5 to 4.0 mm Hg). These findings should be interpreted cautiously, as this study was sufficiently powered to detect larger, clinically meaningful changes but may have lacked power to identify more modest effects.
CONCLUSIONS: This study is the first to assess the causal effects of population-based BMI screening on long-term health outcomes in a Southeast Asian population. The findings suggest that merely informing individuals of their overweight or obese status and implementing light-touch interventions are insufficient to significantly reduce BMI or drive sustained behavior change. Nonetheless, the results do not exclude the possibility of short-term effects, and more frequent or sustained light-touch interventions may still be effective. Future studies should design more intensive interventions and include larger sample sizes.
PMID:41406464 | DOI:10.2196/80381
JMIR Med Educ. 2025 Dec 17;11:e65287. doi: 10.2196/65287.
ABSTRACT
BACKGROUND: Electronic medical records (EMRs) are a potentially rich source of information on an individual’s health care providers’ clinical activities. These data provide an opportunity to tailor web-based learning for health care providers to align closely with their practice. There is increasing interest in the use of EMR data to understand performance and support continuous and targeted education for health care providers.
OBJECTIVE: This study aims to understand the feasibility and acceptability of harnessing EMR data to adaptively deliver a web-based learning program to early-career physicians.
METHODS: The intervention consisted of a microlearning program where content was adaptively delivered using an algorithm input with EMR data. The microlearning program content consisted of a library of questions covering topics related to best practice management of common emergency department presentations. Study participants were early-career physicians undergoing training in emergency care. The study design involved 3 design cycles, which iteratively changed aspects of the adaptive algorithm based on an end-of-cycle evaluation to optimize the intervention. At the end of each cycle, an online survey and analysis of learning platform metrics were used to evaluate the feasibility and acceptability of the program. Within each cycle, participants were recruited and enrolled in the adaptive program for 6 weeks, with new cohorts of participants in each cycle.
RESULTS: Across each cycle, all 75 participants triggered at least 1 question from their EMR data, with the majority triggering 1 question per week. The majority of participants in the study indicated that the online program was engaging and the content felt aligned with clinical practice.
CONCLUSIONS: The use of EMR data to deliver an adaptive online learning program for emergency trainees is both feasible and acceptable. However, further research is required on the optimal design of such adaptive solutions to ensure training is closely aligned with clinical practice.
PMID:41406416 | DOI:10.2196/65287
JMIR Hum Factors. 2025 Dec 17;12:e76804. doi: 10.2196/76804.
ABSTRACT
BACKGROUND: Health messages are integral to smoking cessation interventions. Common approaches to health message development include expert-crafted messages and audience-generated messages, which produce messages that can be monotonic, didactic, and limited in number. We introduce an alternative approach to health message development that relies on user-generated content available on open-content platforms as a source of health messages.
OBJECTIVE: We examined the acceptability of user-generated content curated from Twitter (subsequently rebranded X) as a source of health support messages in a newly developed smoking cessation mobile intervention called Quit Journey and the optimal timing and frequency with which health messages can be deployed to support app users in real time.
METHODS: A total of 12 semistructured focus groups were held with 38 young adults with low socioeconomic status who smoked cigarettes, wanted to quit, and were aged 18 to 29 years. Focus groups were held virtually on GoTo Meeting, audio recorded, and transcribed verbatim. Deductive thematic analysis was used, with themes based on 5 constructs from the second unified theory of acceptance and use of technology (ie, effort expectancy, facilitating conditions, hedonic motivation, performance expectancy, and social influence) and negative, neutral, and positive sentiment.
RESULTS: Participants perceived user-generated content positively (56/108, 51.9% of the quotes) and focused on their perceived usefulness (37/108, 34.3% of the quotes). User-generated content was perceived as authentic, nonrepetitive support from people with similar real-life experiences. Negative or sarcastic user-generated content elicited negative reactions from participants. Participants preferred receiving 3 or fewer daily messages, ideally before cravings. Suggestions focused on the need to screen user-generated content before its inclusion in the app library and allow app users to customize message frequency and timing.
CONCLUSIONS: User-generated content was deemed an acceptable source of health messages. This content can improve the efficacy and effectiveness of smoking cessation interventions by increasing their pool of unique messages that may be better received and more persuasive than expert-curated content. User-generated content can be used to curate health messages for all medical conditions and behaviors with relevant publicly available online content for integration in behavioral interventions given its high volume, brevity, and narrative-like nature. Future research is needed to investigate the effects of user-generated content on health behaviors and identify the theoretical mechanisms for these effects.
PMID:41406415 | DOI:10.2196/76804
JMIR Med Inform. 2025 Dec 17;13:e79748. doi: 10.2196/79748.
ABSTRACT
BACKGROUND: Knee cartilage injury (KCI) poses significant challenges in the early clinical diagnosis process, primarily due to its high incidence, the complexity of healing, and the limited sensitivity of initial imaging modalities.
OBJECTIVE: This study aims to employ magnetic resonance imaging and machine learning methods to enhance the classification accuracy of the classifier for KCI, improve the existing network structure, and demonstrate important clinical application value.
METHODS: The proposed methodology is a multidimensional feature cross-level fusion classification network driven by the large separable kernel attention, which enables high-precision hierarchical diagnosis of KCI through deep learning. The network first fuses shallow high-resolution features with deep semantic features via the cross-level fusion module. Then, the large separable kernel attention module is embedded in the YOLOv8 network. This network utilizes the combined optimization of depth-separable and point-by-point convolutions to enhance features at multiple scales, thereby dramatically improving the hierarchical characterization of cartilage damage. Finally, five classifications of knee cartilage injuries are performed by classifiers.
RESULTS: To overcome the limitations of network models trained with single-plane images, this study presents the first hospital-based multidimensional magnetic resonance imaging real dataset for KCI, on which the classification accuracy is 99.7%, the Kappa statistic is 99.6%, the F-measure is 99.7%, the sensitivity is 99.7%, and the specificity is 99.9%. The experimental results validate the feasibility of the proposed method.
CONCLUSIONS: The experimental outcomes confirm that the proposed methodology not only achieves exceptional performance in classifying knee cartilage injuries but also offers substantial improvements over existing techniques. This underscores its potential for clinical deployment in enhancing diagnostic precision and efficiency.
PMID:41406414 | DOI:10.2196/79748
Neurology. 2026 Jan 27;106(2):e214343. doi: 10.1212/WNL.0000000000214343. Epub 2025 Dec 17.
ABSTRACT
BACKGROUND AND OBJECTIVES: The association between dairy intake and dementia risk remains uncertain, especially for dairy products with varying fat contents. The aim of this study was to investigate the association between high-fat and low-fat dairy intake and dementia risk.
METHODS: This study used data from a prospective cohort in Sweden, the Malmö Diet and Cancer cohort, which consisted of community-based participants who underwent dietary assessment at baseline (1991-1996). Dietary intake was evaluated using a comprehensive diet history method that combined a 7-day food diary, a food frequency questionnaire, and a dietary interview. Dementia cases were identified through the Swedish National Patient Register until December 31, 2020, and cases diagnosed until 2014 were further validated. The primary outcome of the study was all-cause dementia, and the secondary outcomes were Alzheimer disease (AD) and vascular dementia (VaD). Cox proportional hazard regression models were used to estimate hazard ratio (HR) and 95% CI.
RESULTS: This study included 27,670 participants (mean baseline age 58.1 years, SD 7.6; 61% female). During a median of 25 years of follow-up, 3,208 incident dementia cases were recorded. Consumption of ≥50 g/d of high-fat cheese (>20% fat) was associated with a reduced risk of all-cause dementia (HR 0.87; 95% CI, 0.78-0.97) and VaD (HR 0.71, 95% CI 0.52-0.96) compared with lower intake (<15 g/d). An inverse association between high-fat cheese and AD was found among APOE ε4 noncarriers (HR 0.87, 95% CI 0.76-0.99, p-interaction = 0.014). Compared with no consumption, individuals consuming ≥20 g/d of high-fat cream (>30% fat) had a 16% lower risk of all-cause dementia (HR 0.84, 95% CI 0.72-0.98). High-fat cream consumption was inversely associated with the risk of AD and VaD. Consumption of low-fat cheese, low-fat cream, milk (high-fat and low-fat), fermented milk (high-fat and low-fat), and butter showed no association with all-cause dementia.
DISCUSSION: Higher intake of high-fat cheese and high-fat cream was associated with a lower risk of all-cause dementia, whereas low-fat cheese, low-fat cream, and other dairy products showed no significant association. APOE ε4 status modified the association between high-fat cheese and AD. Our study’s observational design limits causal inference.
PMID:41406402 | DOI:10.1212/WNL.0000000000214343
J Am Acad Orthop Surg. 2025 Dec 8. doi: 10.5435/JAAOS-D-25-00380. Online ahead of print.
ABSTRACT
OBJECTIVES: Increased mortality following geriatric hip fractures is well reported. However, population-level analysis of mortality trends over time are not common. This study aimed to evaluate the 3- and 12-month mortality after geriatric hip fractures from 2010 to 2019.
METHODS: The New York Statewide Planning and Research Cooperative System database from 2010 to 2020 was retrospectively queried for patients aged >65 years with a femoral neck or intertrochanteric hip fracture. Kaplan-Meier survival analysis was used to calculate mortality rates for each year. Cox proportional hazard multivariable regression controlling for sex, age, race, obesity, smoking, and Elixhauser comorbidity index was used to compare mortality hazard ratios for each year. Secondary outcomes included length of stay, discharge disposition, and 3-month readmission and emergency department visits.
RESULTS: From 2010 to 2019, 142,540 patients aged ≥65 years had a diagnosis of femoral neck fracture (62%) or intertrochanteric hip fracture (38%). The mean age was 83.29 years (SD 8.22). The mean Elixhauser comorbidity index was 7.35 (SD 7.60). Kaplan-Meier survival analysis revealed that for the complete cohort 3-month mortality rate was 9.82% (95% confidence interval 9.65% to 9.98%) and 12-month mortality rate was 16.06% (95% confidence interval 15.84% to 16.27%). The 3-month mortality rate went from 10.8% in 2010 to 8.6% in 2019 and the 12-month mortality rate went from 17.7% in 2010 to 14.8% in 2018 before rising to 16.9% in 2019. Cox multivariate proportional hazard regression demonstrated statistically significant decreased hazard ratio from 2012 to 2019 compared with reference hazard in 2010 (all P < 0.05). Reductions were also observed for length of stay (7.8 to 6.4 days, P < 0.001), 3-month readmissions rate (34% to 22%, P < 0.001), and 3-month emergency department visit rate (45% to 34%, P < 0.001).
CONCLUSION: Mortality after geriatric hip fractures has demonstrated a reduction in the past decade with 3-month mortality continuously decreasing from 2010 to 2019 and 12-month mortality decreasing from 2010 to 2018 before increasing in 2019.
PMID:41406399 | DOI:10.5435/JAAOS-D-25-00380
J Cardiovasc Pharmacol. 2025 Nov 25. doi: 10.1097/FJC.0000000000001780. Online ahead of print.
ABSTRACT
Colchicine has been studied as an anti-inflammatory treatment for cardiovascular prevention, but findings from randomized trials have been inconsistent. This meta-analysis evaluated the efficacy and safety of colchicine in reducing major adverse cardiovascular events (MACE) and its individual components, using ChatGPT as an assistant throughout the process. Randomized trials of colchicine for cardiovascular prevention were systematically identified, and data extraction, risk of bias assessment, and meta-analyses were performed with ChatGPT under human supervision. The primary outcome was MACE, while secondary outcomes included myocardial infarction (MI), stroke, revascularization, cardiovascular mortality, and all-cause mortality. Eleven trials involving 30,888 patients were included. Colchicine significantly reduced MACE (risk ratio 0.75, 95% CI 0.63-0.88), though no significant effects were observed for MI, stroke, cardiovascular mortality, or all-cause mortality. In addition to its clinical findings, this study illustrates the potential of ChatGPT to assist in systematic reviews and meta-analyses by automating screening, data extraction, bias assessment, and statistical code generation. This integration reduced researcher time by over 70% while maintaining accuracy through human validation. Overall, colchicine appears to lower the risk of MACE but the results of the CLEAR trial have lowered certainty, while the findings highlight the feasibility and efficiency gains of using large language models in evidence synthesis workflows.
PMID:41406368 | DOI:10.1097/FJC.0000000000001780
Can Urol Assoc J. 2025 Dec 15. doi: 10.5489/cuaj.9370. Online ahead of print.
ABSTRACT
INTRODUCTION: Accurate pre-intervention staging of upper tract urothelial carcinoma (UTUC) remains a significant clinical challenge, particularly in identifying muscle-invasive disease (≥pT2), where kidney-sparing surgery may not be appropriate. Current imaging and biopsy approaches are often inadequate. Radiomics, which extracts high-dimensional features from medical imaging, may improve non-invasive staging. This study assessed whether computed tomography (CT)-based radiomic features, alone or combined with clinical data, could predict ≥pT2 UTUC in a multicenter Canadian cohort.
METHODS: We retrospectively analyzed clinical, pathologic, and radiographic features of patients with UTUC who underwent extirpative surgery at five academic centers from January 2, 2001, to May 1, 2023. Radiomic features were extracted from machine-learning segmentations of the affected kidney using the excretory phase of CT. Predictive models were developed using clinical only, radiomic only, and combined data to predict stage ≥pT2. Feature selection included univariable logistic regression, correlation filtering, and LASSO. Model performance was assessed via five-fold cross-validation repeated 10 times, with area under the curve (AUC) as the primary metric.
RESULTS: Of 441 patients, 208 (47.2%) were included. Of the 208 patients, 97 (46.6%) had ≥pT2 disease. The clinical model (AUC 0.602) included age, hydronephrosis, and high-grade cytology. The radiomics model, based on two texture features, achieved an AUC of 0.653. The combined model achieved an AUC of 0.647. Radiomics and combined models significantly outperformed the clinical model (p<0.01), but did not differ from each other. For 117 patients with renal pelvis cancers, the combined model’s discrimination performance was statistically better than the clinical model (AUC 0.708 vs. AUC 0.607, p<0.001). Likewise, the radiomics’ AUC discrimination performance was statistically better than the clinical model (AUC 0.694 vs. AUC 0.607, p=0.004). In contrast, we found no significant difference in model performance in the non-renal pelvis subgroup (n=91).
CONCLUSIONS: Conventional radiomics improved the prediction of muscle-invasive UTUC compared to clinical models alone, but overall accuracy remained suboptimal for clinical use. Heterogeneity in CT protocols and challenges with tumor segmentation were the main limitations. Future work should develop more adaptable AI models trained on larger, more diverse datasets to better reflect real-world imaging conditions.
PMID:41406346 | DOI:10.5489/cuaj.9370
Can Urol Assoc J. 2025 Dec 15. doi: 10.5489/cuaj.9451. Online ahead of print.
ABSTRACT
INTRODUCTION: Type and screen testing (T&S) is routinely performed preoperatively for many endoscopic procedures, despite low transfusion rates. While important, T&S can be costly, unnecessary, and burdensome for patients to obtain in a short timeframe due to expiry. We aimed to assess and reduce unnecessary T&S in a safe and collaborative manner through a Transfusion Dashboard. We assessed the effect of reduced testing on patient safety, cost, and the environment.
METHODS: This quality improvement study used the Transfusion Dashboard, a web-based, institutional platform tracking blood transfusion trends. During the observation phase (2016-2019), procedure-specific preoperative T&S recommendations were developed. Following implementation of these recommendations in 2020, the incidence of T&S, perioperative transfusion rates, and rescue transfusion rates were assessed pre- and post-intervention using the Chi-squared test. Cost and environmental savings were also evaluated.
RESULTS: From 2016-2023, outcomes were tracked for 4375 pre-initiative and 2488 post-initiative patients who underwent endoscopic procedures. We found a statistically significant decrease in T&S following initiative implementation for transurethral resection of the prostate (TURP), percutaneous nephrolithotomy (PCNL), holmium e-nucleation of the prostate (HoLEP), and transurethral resection of bladder tumor (TURBT) by as much as 51.2%. There was no change in uncrossed or overall blood transfusions. Since the implementation of the initiative, $45 362.81 in testing materials were saved and an associated reduction of 697 kg CO2 was observed.
CONCLUSIONS: Institutional- and procedure-specific testing guidelines decreased unnecessary tests, leading to improved resource stewardship, reduced cost, improved patient experience, and environmental savings. Initial modest cost savings and care improvements may be amplified safely in larger organizations and across more procedures.
PMID:41406342 | DOI:10.5489/cuaj.9451