Citations

Background: A measles case requires immediate action to identify and administer prophylaxis to exposed individuals in healthcare settings. Infection preventionists (IPs) often balance proactive contact tracing with waiting for a positive test result. Objective: To develop a tool that enables IPs to stratify suspect measles cases by risk level, thereby supporting the decision to prioritize or defer active intervention while awaiting test results. Methods: In spring 2025, we developed a preliminary matrix and used 19 additional suspect patients to further refine the scoring of the tool. In August 2025, we deployed a finalized tool. The two-dimensional risk matrix incorporated both epidemiological risk factors and clinical risk factors, each scored numerically based on yes or no questions. The sum of both categories was used to assign a final risk level that determined the recommendations for IPs (Figure 1). The study population included patients tested for measles during the implementation period. Descriptive statistics were used to summarize the distribution of risk categories and outcomes. Among the whole cohort, Firth’s logistic regression was used to evaluate the association between clinical or epidemiological risk score and measles diagnosis. Among the production cohort, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated to evaluate the diagnostic performance. Results: Since implementation, the matrix was applied to 56 patients tested for measles. Of these, 10.7% (n=6) were categorized as high risk, 32.1% (n=18) as medium, and 57.1% (n=32) as low. Four of the 56 patients were positive for measles, 3 were categorized as high-risk and 1 as medium risk. Each one-unit increase in epidemiological risk was associated with a 2 folds increase in the odds of a positive measles diagnosis (odds ratio = 2.0; 95% CI, 1.3–3.6). The low-risk category had an NPV of 1, indicating that all low?risk patients tested negative, thus supporting safe workload reduction by deprioritizing these cases. Conclusion: The Measles Exposure Response Risk Matrix effectively stratified cases and supported IP prioritization efforts. Low-risk groups required no action, expedited testing was reasonable for medium-risk cases, and proactive actions were reserved for high-risk cases. Clinical and epidemiologic risk scores progressed from arbitrary assignment to validated metrics, confirmed by statistical analysis. The flexible design permits adaptation to other infectious diseases, including avian influenza and mpox. With rising measles incidence, tools like this matrix will be essential to support rapid, evidence-based actions by infection prevention teams.

ABSTRACT Clinicians tend to overestimate their clients’ progress in treatment. A potential solution of adding formalized assessments into treatment has led to concerns about client paperwork burden. In this mixed‐method study, we explored adult clients’ reactions to one formal assessment, the Outcome Questionnaire‐45 (OQ‐45). We used purposeful sampling to select 17 clients at a university counseling training center who had either improved, showed little change, or who deteriorated according to OQ‐45. Using semi‐structured interviews and grounded theory, we identified two higher order categories, clinical benefits and threats to accuracy, and two axial categories, OQ‐45 likability and perceived accuracy. A crossover mixed analysis showed strong correlations between OQ‐45 outcomes, clinical benefits, and OQ‐45 likability. We proposed a conceptual model showing how client attitudes toward the OQ‐45 depended on how compatible outcomes were to clients’ perceived benefits received in counseling. Counseling implications and future research directions are discussed.

ABSTRACT This study aims to develop MRI‐based 3D statistical shape models for patellar dislocation patients and investigate bone shape features that distinguish between single and recurrent dislocation populations, as well as control and patellar dislocation populations. MRIs from 16 single dislocation patients, 17 recurrent dislocation patients, and 20 control subjects were used to build statistical shape models for femur and patella bones. Bone shape features were extracted and tested for statistical significance ( p < 0.05) for distinguishing the single dislocation group from the recurrent dislocation groups, and the control group from the dislocation group. Statistically significant shape features distinguishing the three groups were identified for both bones. A shallower trochlear groove and smaller patellar ridge told the dislocation groups apart from the control group. A prolonged lateral condyle in femur and a laterally shifted ridge and hood shaped lateral facet in patella distinguished the recurrent dislocation group from the single dislocation group. The study's findings from 3D statistical shape modeling of the femur and patella reveal distinct anatomical differences across control, single dislocation, and recurrent patellar dislocation groups. This advanced understanding aids in identifying individuals at higher risk for initial or recurring patellar dislocations, enabling clinicians to tailor treatment and management strategies more effectively to prevent further dislocations and mitigate potential patellofemoral joint damage.

ABSTRACT Training in electrodiagnostic (EDX) medicine is highly variable globally, with trainees receiving widely different levels of didactic education, skills training, and hands‐on experience. Global standardization of EDX training has the potential to improve diagnostic accuracy, promote equitable care, and strengthen international collaboration. Major barriers include limited resources, faculty shortages, and a lack of standardized curricula. To address these disparities, opportunities and strategies for international collaboration are proposed, including developing a uniform content curriculum, competency‐based evaluation frameworks, and integration of online and simulation‐based learning tools.

Background Malpositioned orbital implants are an underappreciated source of significant postoperative morbidity, often necessitating revision surgery. Decision-making regarding practical frameworks to guide revision remains challenging and is not well standardized or characterized. Objective To analyze clinical presentations, radiographic failure patterns, multidisciplinary surgical strategies, and outcomes of patients undergoing revision surgery for malpositioned orbital implants. Methods A single-center retrospective consecutive case series was conducted of patients who underwent removal or replacement of malpositioned orbital implants. Patient demographics, presenting symptoms, radiographic findings, surgical approaches, implant management strategies, and postoperative outcomes were reviewed. Most cases were co-managed by plastic/reconstructive and oculoplastic surgeons. Descriptive statistics were used to summarize findings. Results Five of seven patients (71.4%) were female, with a median age of 60 years (range 29–78). Diplopia was the most common presenting symptom (5/7, 71.4%) along with pain or swelling (5/7, 71.4%) followed by globe malposition (3/7, 42.9%). CT demonstrated implant protrusion into orbital soft tissue in 4/7 patients (57.1%) and peri-implant inflammatory collections in 3/7 (42.9%). Out of our 7 revision cases, four patients (57.1%) underwent explantation alone, and three (42.9%) underwent secondary reconstruction with custom or semi-custom implants. At follow-up, diplopia improved or resolved in 4/5 affected patients (80%), and pain, swelling, and globe position improved in all symptomatic patients. Conclusion Malpositioned orbital implants demonstrate heterogeneous failure mechanisms that require individualized surgical management. A multidisciplinary approach can guide decision-making regarding implant removal versus revision of reconstruction, enabling safe revision surgery and favorable clinical outcomes. Additionally, virtual surgical planning and intraoperative navigation are enablers of safe and precise revision orbital surgery.

Glioblastoma is the most common form of brain cancer in adults and remains one of the most lethal forms of cancer. Treating glioblastoma is uniquely challenging due to its aggressive nature and its critical location, the brain, which must remain largely undamaged. Due to these difficulties, there is a lack of effective treatment options. Computer models simulating tumors and their response to therapy, known as digital twins, provide a promising new strategy. These model systems are mathematical equations that capture the biological knowledge of how cells interact during tumor growth and in response to treatment. By solving these equations, we can create virtual patients that provide accurate predictions of real patients’ tumor progression. This approach has the potential to improve outcomes by designing therapies for the specific needs of each patient, moving beyond the limitations of the “one-size-fits-all” approach that is currently used.

Abstract Purpose Medical school applicants engage in informal preference signaling by submitting letters of intent (which include a commitment to accept an admission offer) and letters of interest (which include no such commitment). Commitments are unenforceable and are based on an honor system. Applicants submit letters to medical schools before receiving financial aid offers, potentially disadvantaging low socioeconomic status (SES) applicants. This study describes postinterview communication patterns and analyzes associations among applicant demographics, letter submission rates, and admissions outcomes. It tests the hypothesis that low SES is negatively associated with letter of intent submission. Method The authors collected postinterview communications from all interviewees at the Mayo Clinic Alix School of Medicine during 3 admissions cycles (2021-2023). Authors developed and validated codebook definitions for the 2 letter types. SES was defined using the Association of American Medical Colleges education-occupation indicator. Letter submission rates were compared using mixed-effects logistic regression. Results Of 1,313 applicants, 1,109 submitted 1 letter or more. The sample included 257 (19.6%) low-SES and 1,056 (80.4%) non–low-SES applicants. Coders showed excellent interrater reliability (Gwet AC1 range, 0.89-0.995). Low-SES applicants submitted letters of intent with similar frequency to higher-SES applicants (33.9% [87/257] vs 33.5% [354/1,056]; odds ratio, 1.02; 95% CI, 0.76-1.37; P = .88) and submitted letters of interest at similar frequency (55.6% [143/257] vs 59.5% [628/1,056]; odds ratio, 0.86; 95% CI, 0.65-1.13; P = .27). Among 116 applicants who wrote letters of intent and were offered admission, 20 (17.2%) declined the admission offer. Conclusions There was no difference in letter of intent submission based on SES, and letter of intent submission was positively associated with matriculation. Among letter of intent writers who were offered admission, 1 in 6 declined. Undergraduate medical education may wish to consider a formalized preference signaling system to promote an honest and transparent application process.

Heart failure with preserved ejection fraction (HFpEF) is associated with exercise intolerance, with respiratory muscle dysfunction as a contributing factor. Patients with HFpEF have exaggerated cardiovascular responses to respiratory muscle metaboreflex activation. In this study, we determined the impact of inspiratory muscle training (IMT) on the respiratory muscle metaboreflex in HFpEF. We found that 8 wk of IMT improved inspiratory muscle strength and attenuated the respiratory muscle metaboreflex-mediated cardiovascular responses in patients with HFpEF.

Abstract Objectives Accurate and timely prediction of physiological deterioration in hospitalized patients is essential, but existing deep learning models, which rely solely on structured electronic health record (EHR) data, generate high false-positive rates by missing crucial contextual information in clinical notes. We present DETERIO-LLM, a hybrid prediction model that selectively integrates large language models (LLMs) with a deep learning model (DETERIO) to reclassify borderline-risk alerts using narrative clinical notes. Materials and Methods This retrospective study used a cohort of 1000 inpatients (4.6% deterioration prevalence). DETERIO-LLM selectively analyzed narrative notes for alerts in the uncertainty range (scores 2.5-3.5). Performance was evaluated using sensitivity, positive predictive value (PPV), and F1 score, benchmarked against comparators including the best-established ML model, eCART. Results DETERIO-LLM significantly improved predictive precision over all comparators. At its optimal threshold, the model achieved a PPV of 30.6% and an F1 score of 37.3%, outperforming eCART. This selective integration reduced false-positive alerts by 46.5% within the uncertainty range while maintaining comparable sensitivity. Discussion This study demonstrates a practical, interpretable method for integrating LLM-derived contextual knowledge into existing structured EHR prediction workflows. By focusing LLM analysis on high-uncertainty alerts, DETERIO-LLM substantially improves clinical utility through a marked reduction in false-positive cases, which is critical for mitigating alert fatigue. Conclusion Selective LLM integration with structured EHR models offers a viable, interpretable path to improving early warning systems in clinical care by leveraging narrative context to reduce alert fatigue while enhancing predictive performance.

Abstract More information is needed to better understand the effect of caffeine on sleep quality at the community level. In a population-based, cross-sectional study design, we aimed to assess the effect of caffeine intake on sleep quality by the use of a multivariate exposure-effect model, adjusted for relevant confounders. All Atahualpa residents aged ≥40 years were identified during a door-to-door survey and interviewed with the Pittsburgh Sleep Quality Index (PSQI) and a structured instrument designed to estimate the daily amount of caffeine intake. An exposure-effect model was built using augmented inverse probability weighting taking into account variables that were associated with exposure (using a probit model) and variables that were associated with outcome (in a linear model). Out of 779 eligible individuals, 716 (92%) were included. Consumption of <100 mg/day of caffeine was recorded in 320 (45%) participants, from 100 to 200 mg/day in 299 (42%), and >200 mg/day in 97 (13%). Mean score in the PSQI was 4.5±2.2 points, with 203 (28%) individuals classified as poor sleepers (≥6 points). The exposure-effect model, adjusted for variables associated with the exposure (symptoms of depression, total cholesterol blood levels and smoking) and the outcome (age, symptoms of depression, physical activity and fasting glucose levels), revealed no effect of caffeine intake in sleep quality (average exposure effect: 0.027, 95% C.I.: −0.284 to 0.338, p=0.866). This population-based study shows that caffeine intake has no effect on sleep quality in community-dwelling adults living in a rural village of Ecuador.