Nevin Manimala

MSMR. 2026 May 15;33(4):10-14.

ABSTRACT

This study utilized de-identified surveillance data to estimate the incidence of head and neck cancer among active component service members (Army, Navy, Air Force, Marine Corps, Coast Guard) from 2010 through 2024. This report updates the June 2021 MSMR analysis of oral and pharyngeal cancers (2007-2019) by expanding the case definition to include all head and neck cancers and extending the surveillance period through 2024. There were 549 cases of head and neck cancer diagnosed in the active component military during the 15-year period of analysis. The Army had the highest 15-year incidence rate (3.3 per 100,000 person-years) compared to the Navy (2.6 per 100,000), Air Force (2.6 per 100,000), Coast Guard (2.0 per 100,000), and Marine Corps (1.3 per 100,000). Service members ages 40 years and older had the highest overall incidence rate (12.3 per 100,000), which was 3.3 times the next highest rate observed among those ages 35-39 years. The 15-year male incidence rate (2.9 per 100,000) was greater than that among females (1.7 per 100,000). The parotid gland was the most common site of diagnosis, comprising 14.8% of cases. This report provides the most current head and neck cancer incidence data for active component service members from 2010 through 2024; it establishes baseline rates for monitoring of future trends and highlights specific high-risk populations (e.g., men, Army personnel, service members ages 40 years and older). Although head and neck cancer is the seventh most prevalent cancer worldwide, its incidence among active component service members is seldom reported. Head and neck cancer is often not diagnosed until it has metastasized. Significant physical limitations (e.g., difficulty chewing, speaking, and swallowing) and psychosocial effects (e.g., anxiety, depression, social isolation), compromising service member readiness, can accompany this type of cancer.

PMID:42155135

MSMR. 2026 May 15;33(4):3-9.

ABSTRACT

Military service members remain a priority population for assessing the prevalence, patterns, and long-term consequences of tobacco and nicotine use. The limitations inherent to documenting use among military service members, however, complicate the design of exposure assessment. This study combined 2 data sources-by aggregating self-reported Periodic Health Assessment (PHA) survey data with International Classification of Diseases, 9th and 10th revisions, Clinical Modification (ICD-9-CM/ICD-10-CM) medical diagnostic codes-to classify nicotine and tobacco use as exposures delineated by recent use or history of any use. The study population included a total of 921,394 U.S. active component service members who completed a PHA in 2023. PHA classification for ‘recent use’ was defined by self-reported use of any tobacco or nicotine product within the past 30 days, whereas ‘history of any use’ included recent users in addition to those who reported cessation of use. The full roster of service members who completed the PHA in 2023 was matched to ambulatory and inpatient medical records within 30 days, before or after, the PHA sample period (December 1, 2022-January 31, 2024) to identify selected ICD-10-CM codes for recent use. Selected diagnostic codes for a ‘history of any use’ were queried for a period of 20 years preceding and 30 days following (January 1, 2004-January 31, 2024) the PHA sample period. Among PHA respondents, 22.0% (n=203,156) self-reported recent nicotine or tobacco use. When aggregating PHA data with recent exposure classified from diagnostic codes, the resulting assessment of recent nicotine or tobacco use increased to 28.7% (n=264,194). Critically, this aggregation identified 61,038 U.S. service members with no evidence of recent use on the PHA but with a concurrent clinical record during the specified matching period. Aggregating data sources for a history of any use only nominally improved the estimate, increasing it from 41.1% (PHA alone) to 43.1%. Agreement between sources was fair for both recent use (κ=0.28) and historical use (κ=0.36). The results of this study indicate that neither self-reported PHA data nor medical diagnostic codes alone provide a complete picture of tobacco and nicotine use among U.S. active component service members. The combination of medical diagnostic codes with self-reported PHA survey responses increases exposure estimates of recent tobacco or nicotine use among U.S. active component service members to 28.7%, in comparison to 22.0% if exclusively assessing recent use from the PHA. The integration of multiple data sources may provide a more comprehensive assessment of recent nicotine and tobacco exposure among service members, directly supporting enhanced public health surveillance.

PMID:42155134

Port J Card Thorac Vasc Surg. 2026 May 10;33(1):19-23. doi: 10.48729/pjctvs.607.

ABSTRACT

INTRODUCTION: The management of chest tubes after pulmonary resection remains non- standardized, and suction levels are often determined by the surgeon’s preference. This retrospective study aimed to compare the clinical outcomes of low suction -2cmH2O-2cmH2​O versus the conventional suction level used in our institution -15cmH2O-15cmH2​O using digital drainage devices after videoassisted thoracic surgery (VATS) lobectomy for suspected or confirmed lung cancer in a thoracic surgery centre.

METHODS: We analysed 120 patients who underwent pleural drainage after VATS lobectomy between January 2023 and September 2024. The primary outcome was drainage duration. Secondary outcomes included hospital stay, prolonged air leak, complications, and readmissions.

RESULTS: No significant differences were observed in drainage duration (2.0 vs. 4.0 days; p=0.125p=0.125) or hospital stay (3.0 vs. 4.0 days; p=0.104p=0.104 ). The incidence of prolonged air leak was similar between groups (20.3% vs. 24.6%; p=0.578p=0.578 ). However, subcutaneous emphysema occurred more frequently in the low suction group (22% vs. 8.2%; p=0.04p=0.04 ), with a higher need for intervention, despite comparable baseline forced expiratory volume in the first second (FEV1) values between suction level groups. Importantly, patients who developed subcutaneous emphysema had significantly lower baseline FEV1 values, regardless of suction level. COPD was identified as a significant predictor of longer drainage duration, longer hospital stay, and higher complication rates.

CONCLUSION: Although suction level did not significantly influence postoperative recovery, the higher incidence of subcutaneous emphysema in the low suction group warrants further investigation. The presence of COPD and impaired baseline lung function should be considered when selecting suction levels after VATS lobectomy.

PMID:42155129 | DOI:10.48729/pjctvs.607

JMIR Hum Factors. 2026 May 19;13:e73369. doi: 10.2196/73369.

ABSTRACT

BACKGROUND: Mental health problems are prevalent among young people aged 16 to 24 years. With the health care system prioritizing severe cases, most young people wait months before accessing professional support. One-to-one helplines offer alternative and accessible mental health services for young people with emotional support, psychoeducation, and signposting. Signposting empowers young people to access long-term support beyond a brief helpline session. However, young people often choose not to access the signposts. Despite its importance, there is a dearth of existing research examining signposting via digital mental health helplines for young people.

OBJECTIVE: Using cross-sectional survey data from The Mix, a UK charity supporting young people aged 25 years or younger, this study conducted a mixed methods analysis of their multichannel (webchat, email, telephone, and web-based contact form) helpline survey between February 2020 and October 2023.

METHODS: The analytic sample included 296 participants who collectively received 872 signposts (approximately 872/4500, 19% of signposts provided during the survey collection period), of which 822 with complete outcome data were included in the statistical models. Multinomial logistic regressions were conducted to examine whether young people’s use and perceived usefulness of the signposts they received differed across modes of delivery and their demographic characteristics (gender, ethnicity, and age). Qualitative thematic analysis of 106 open-ended responses from 97 participants was also examined to illuminate why young people found signposting helpful and how it could be improved.

RESULTS: In the overall model, which included all predictors, webchat users identifying as White, women, and aged 16-19 years were significantly more likely to use and find signposts helpful than to perceive them as unhelpful (odds ratios [OR] 0.28, 95% CI 0.17-0.46; P<.001), not intend to use them (OR 0.13, 95% CI 0.07-0.26; P<.001), or only plan to use them later (OR 0.29, 95% CI 0.18-0.46; P<.001). Thematic analysis of open-ended responses revealed that young people found the choice of signposts relevant and appreciated how signposting was integrated with emotional support. Young people also felt more hopeful after being signposted and gained both clarity and insight into the support available. However, they also noted challenges, such as feeling overwhelmed or encountering outdated signposts.

CONCLUSIONS: Given the increasing reliance on digital mental health services, ensuring that signposting remains accessible, relevant, and tailored to diverse user needs is essential. By optimizing signposting strategies, helplines can empower young people to seek appropriate long-term support, ultimately improving mental health outcomes.

PMID:42155126 | DOI:10.2196/73369

JMIR Form Res. 2026 May 19;10:e84249. doi: 10.2196/84249.

ABSTRACT

BACKGROUND: Despite the notable proliferation of smoking cessation mobile apps, to date, no validated, Spanish-language, culturally tailored mobile intervention exists for Spanish speakers in the United States.

OBJECTIVE: The aim of this study was to conduct formative research to inform the adaptation of an evidence-based smoking cessation intervention developed for Spanish-speaking Hispanic and Latino individuals from a printed format into a mobile app.

METHODS: Guided by a user-centered approach and in collaboration with product design industry experts, wireframes were developed to present the app’s layout and functionality. Focus groups were conducted over Zoom (Zoom Communications) with Spanish-speaking individuals who currently smoke to assess their previous mobile app experience, attitudes toward mobile apps, and feedback on app architecture and design. Two independent reviewers (RB in collaboration with another member from the qualitative core) trained in qualitative methods coded the focus group data using a thematic analysis approach and identified emerging themes.

RESULTS: The app wireframes included 4 navigation buttons on the home screen to organize and deliver evidence-based intervention content-Home (Inicio), Learn (Aprende), My Coach (Mi Couch), and Profile (Perfil). Different wireframe designs were generated in distinct color palettes. Data saturation was reached after three focus groups. Participants were 54% (7/13) women, had a mean age of 56 (SD 14.9) years, 39% (5/13) had an education ≤high school, and 31% (4/13) were married or cohabitating. All participants smoked daily, a mean of 14 (SD 7.8) cigarettes per day, for 32 (SD 16.9) years, and 54% (7/13) smoked ≤30 minutes of waking. Participants reported using social media, news, shopping, and gaming apps, but few used mobile health apps. Salient barriers for app use included worries regarding privacy breaches and fears about misinformation. Desired features included community-building elements, personalization, reward badges, knowledge checks, and audiovisual presentation of content within the app. Participants disliked having a countdown to quit date, preferring an “I quit” button to initiate monitoring progress. They also viewed sharing progress with support networks as a source of unwanted pressure, although a few saw it as motivational. Overall, participants liked the app design and indicated willingness to use it.

CONCLUSIONS: This formative research provides critical insights into preferences related to the development of culturally tailored mobile smoking cessation interventions for Spanish-speaking individuals. Key findings highlighted enthusiasm for a smoking cessation app and the importance of including features that foster social connection and allow for personalization.

PMID:42155124 | DOI:10.2196/84249

JMIR Med Inform. 2026 May 19;14:e84916. doi: 10.2196/84916.

ABSTRACT

BACKGROUND: This exploratory study investigated the impact of computer use on physician performance during clinical simulations. Standardized patient (SP) scenarios used in family practice certification examinations were adapted to include the use of the electronic health record (EHR).

OBJECTIVE: The goal was to compare the impact of EHR use during simulated virtual patient encounters on resident physicians’ and staff physicians’ patient-centeredness (PC) and overall clinical performance, as well as to measure the cognitive load (CL) imposed by EHR use.

METHODS: Sixteen participants each completed 2 video telemedicine simulations with SPs. One simulation case included limited past medical history for the SP in the EHR, while the other did not. Participants were instructed to completely document the encounter using the EHR. Participants’ self-perceived CL was measured using the raw National Aeronautics and Space Administration Task Load Index (NASA-TLX). Video recordings were analyzed for participant PC and overall clinical performance. In addition to interacting with the EHR, multiple participants also conducted internet searches. The proportion of time that participants spent interacting with the computer, either using the EHR or searching the internet, was calculated. Inductive qualitative coding of a subset of video recordings (18 of 32 encounters) was performed, with a focus on signs of stress/CL. All videos were assessed for usability problems.

RESULTS: Staff physicians (n=6) scored higher on PC compared to resident physicians (n=10) for both cases, though differences were not statistically significant after correction for multiple comparisons (family-wise error rate). Physicians’ overall CL, as measured by the raw NASA-TLX, was not significantly correlated with computer use. Exploratory qualitative data analysis found both verbal and nonverbal signs of stress/CL due to computer use while interacting with the SPs. The proportion of time displaying nonverbal signs of stress/CL was calculated for a subset of participants (6 resident physicians and 3 staff physicians). Participant interpretations of instructions to completely document the encounter using the EHR varied widely. It is likely that participants’ usual style of documenting, either primarily during or after patient encounters, impacted their use of the EHR while SPs were present.

CONCLUSIONS: Use of the computer during video telemedicine appointments may negatively impact physician PC and overall clinical performance. Exploratory qualitative coding identified both verbal and nonverbal signs of stress/CL when participants interacted with the computer and the patient simultaneously. Increased clinical experience helped to mitigate the negative impact of computer use. If the use of the EHR is included in physician certification examinations, clear instructions regarding which tasks must be completed in the EHR during interactions with SPs should be provided.

PMID:42155120 | DOI:10.2196/84916

JMIR Med Educ. 2026 May 19;12:e84942. doi: 10.2196/84942.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) tools have revolutionized various aspects of education and health care in recent years. Their influence extends across multiple domains of medical education, from traditional learning to research and foreign language acquisition.

OBJECTIVE: This study aims to evaluate the experiences and perceptions of AI tools usage in a low-resource setting and identify the factors influencing their adoption.

METHODS: A cross-sectional study was conducted to evaluate the experiences with AI tools and perceptions regarding their future applications in education and health care among medical students in Syria. The sample was equally divided between clinical-year students and graduates. Chi-square tests analyzed differences based on demographics and experience, while Mann-Whitney U tests compared group perceptions of AI’s future role. Factors studied included academic year, gender, German language learning, computer access, and research experience.

RESULTS: Among 400 participants, AI tools were widely used for study preparation (228/400, 57% of participants), assignments (160/400, 40% of participants), and research. Clinical students used AI more than graduates for examination preparation (P<.001), creating cases (P=.03), and writing tasks (P<.001). Males used AI more for research (P=.004) or anatomy (P=.02); German learners relied on AI for language tasks. Despite 76% (304/400) of students believing AI would enhance residency training and 71.8% (287/400) of students supporting institutional policies, only 25.5% (102/400) of students expected career benefits. Ethical concerns were higher among females and researchers.

CONCLUSIONS: This study highlights the increasing reliance on AI tools among medical students and graduates for academic and clinical purposes. The highest usage was reported in study preparation, writing tasks, and clinical simulations. Significant differences in AI usage were observed based on academic level, gender, access to technology, and research experience. While perceptions were largely positive, concerns remained around ethical use, potential job displacement, and diminished human interaction in medicine. These findings underscore the importance of developing institutional policies to guide the ethical and effective integration of AI in medical education.

PMID:42155108 | DOI:10.2196/84942

JMIR Form Res. 2026 May 19;10:e91021. doi: 10.2196/91021.

ABSTRACT

BACKGROUND: Clinical decision-making training in operative dentistry commonly relies on real or standardized patients to develop undergraduate students’ ability to deliver safe, effective, and patient-centered care. However, training with real or standardized patients can be limited in scalability, cost-effectiveness, and accessibility. Large language models, with their human-like language capabilities, may have the potential to simulate patients in clinical encounters and help overcome some limitations associated with traditional training approaches.

OBJECTIVE: This study aimed to evaluate the feasibility of using large language model-based standardized virtual patients to support undergraduate dental students’ clinical decision-making training in operative dentistry.

METHODS: This mixed methods cross-sectional feasibility study was conducted during a simulation-based clinical decision-making training session in the Operative Dentistry and Cariology course at the College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia. Eligible participants were second-year undergraduate dental students enrolled in the course. A convenience sampling approach was used, with all eligible students (N=50) invited to participate. A total of 41 students completed the study, 23 (56%) of whom were male. The students were divided into 8 groups. Each group interacted with 2 standardized virtual patients powered by ChatGPT-4o (OpenAI) through the Chatbase platform to complete comprehensive history-taking and then reviewed the standardized virtual patients’ intraoral photographs and bitewing radiographs. For each standardized virtual patient, students as a group recorded diagnoses, performed a risk assessment, and formulated a treatment plan. Students then completed the Student Satisfaction and Self-Confidence in Learning questionnaire. The quality of the standardized virtual patient responses and overall dialogue realism were evaluated using the Dialogue Authenticity Scale. The dialogues were also thematically analyzed to identify authenticity-undermining response features and explore their context and underlying causes.

RESULTS: Students perceived the simulation-based training session positively, with all questionnaire items showing high median scores (4.00-5.00 on a 5-point scale), and both item-level IQRs and 95% CIs spanning no more than 1.0 scale point. In addition, standardized virtual patient responses were largely authentic, with an overall median authenticity rating of 4.50 (IQR 4.00-5.00; 95% CI 4.00-5.00) on a 6-point scale across all interactions. However, several authenticity-undermining response features were identified, including responses that were inconsistent with typical human behavior, contained information beyond a patient’s likely knowledge, or were factually incorrect.

CONCLUSIONS: This proof-of-concept study supports the feasibility of implementing large language model-based standardized virtual patients in undergraduate simulation-based clinical decision-making training in operative dentistry. In a dental context where this application has been only minimally evaluated, this study provides early evidence of positive student perceptions, acceptability, and largely authentic dialogue, while also identifying important performance limitations. Further research is warranted to optimize performance and to evaluate the educational effectiveness of this approach in improving undergraduate students’ clinical skills and knowledge.

PMID:42155101 | DOI:10.2196/91021

JMIR Form Res. 2026 May 19;10:e86025. doi: 10.2196/86025.

ABSTRACT

BACKGROUND: Growing evidence suggests that disruptions in rest-activity rhythms may serve as relevant markers of posttraumatic stress disorder (PTSD). Despite the emergence of machine learning methods applied to actigraphy and self-report data, few studies have used these approaches to identify individuals with clinically diagnosed PTSD. Prior work has focused on predicting probable PTSD based on self-report measures, yet discrepancies exist between clinical diagnoses and probable PTSD derived from self-reports.

OBJECTIVE: This study explored whether wrist actigraphy and sleep logs could be used to accurately predict clinician-rated PTSD diagnosis and probable diagnosis of PTSD based on established self-report cutoffs (PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [PCL-5] ≥31 and ≥38) among trauma-exposed service members and veterans. We also explored which features were most strongly predictive of each outcome and whether models were able to predict PTSD diagnosis even when accounting for other mental health disorders.

METHODS: Wrist actigraphy data and daily sleep logs were collected over 1 week from trauma-exposed male service members and veterans (N=36; mean age 41, SD 5.3 y). Candidate features were identified using univariate feature selection. Extreme gradient boosting models were trained using leave-one-subject-out cross-validation to predict the diagnosis of PTSD and probable diagnosis of PTSD based on 2 self-report cutoffs (PCL-5≥31 and ≥38). Performance metrics were then calculated at the person level. Linear regression was used to assess the discriminant validity of model-predicted scores and each PTSD outcome specifically, relative to other mental health diagnoses.

RESULTS: Machine learning models predicting PTSD diagnosis and probable PTSD based on the PCL-5≥31 threshold demonstrated satisfactory performance in this sample. The diagnosis model achieved an area under the curve (AUC) of 0.83 (95% CI 0.61-1.00), with high accuracy (88%) and specificity (96%) and moderate sensitivity (63%). The PCL-5≥31 model yielded comparable performance (AUC=0.84, 95% CI 0.71-0.98) with balanced sensitivity (73%) and specificity (82%). For both models, a combination of subjective and objective features was the most impactful. These models were able to predict PTSD even when accounting for non-PTSD mental health diagnoses, as model-predicted scores were significantly associated with 2 outcomes: clinician-rated PTSD (B=0.19; P=.002) and probable PTSD based on a PCL-5≥31 cutoff (B=0.24; P=.003). In contrast, the model predicting probable PTSD based on the PCL-5≥38 threshold performed poorly (AUC=0.47, 95% CI 0.24-0.69), with a nonsignificant relationship between model-predicted scores and the outcome (B<0.01; P=.89).

CONCLUSIONS: Both subjective and objective rest-activity features may improve the prediction of PTSD. Further research is needed to validate these findings and explore the use of integrating wearable sensor data and subjective information to support PTSD assessment.

PMID:42155096 | DOI:10.2196/86025

JMIR Form Res. 2026 May 19;10:e89306. doi: 10.2196/89306.

ABSTRACT

BACKGROUND: Pediatric emergency departments see a high volume of patients. Given that children often cannot describe their condition and there is a shortage of nursing staff, it is essential to identify the early warning signs of adverse conditions among children as quickly as possible. Current targeted care needs to be improved.

OBJECTIVE: This study aimed to investigate the application of an artificial intelligence (AI)-based version of the Pediatric Early Warning Score (PEWS) in a pediatric emergency observation unit, analyze the relationship between PEWS and disease severity, and assess its impact on the length of hospital stay and hospitalization costs after admission, thereby providing a reference for targeted nursing care.

METHODS: We performed a retrospective study. A total of 1233 pediatric patients admitted via the pediatric emergency department of a tertiary specialty hospital in Guangzhou from September 2023 to March 2024 were included. The patients were divided according to whether they triggered a PEWS early warning into an early warning group (PEWS ≥1) and a non-early warning group (PEWS=0) during emergency observation. Length of stay and hospitalization costs were compared between the early warning group and the non-early warning group. Differences between groups were assessed using the Mann-Whitney U test. We performed multivariable logistic regression to discuss the association of resource use metrics and PEWS status, adjusted by age, sex, and disease category (respiratory, neurological, and hematologic).

RESULTS: Of 1233 patients, 597 (48.4%) triggered the PEWS early warning (mean score 2.44, SD 1.41), and 636 (51.6%) did not. In the early warning group, 68 children were transferred to the intensive care unit, with a mean PEWS of 3.32 (SD 1.73). Compared with the non-early warning group, the early warning group had a longer hospital stay (z=-5.180; P<.001) and higher hospitalization costs (z=-6.500; P<.001), and the differences between groups were statistically significant (P<.001). Among the top 3 admission categories-respiratory, neurological, and hematologic diseases-children in the early warning group had significantly longer hospital stays and higher hospitalization costs (all P<.01). The β coefficient for length of hospital stay was 0.053 (SE 0.010; Wald χ²1=5.533; odds ratio 1.055, 95% CI 1.035-1.075), while the β coefficient for hospitalization costs was 0.001 (SE 0.000; Wald χ²1=6.075; odds ratio 1.001, 95% CI 1.001-1.001).

CONCLUSIONS: Compared with the non-early warning group, the early warning group had significantly longer hospital stays and higher hospitalization costs (P<.001); similar patterns were observed within respiratory, neurological, and hematologic disease categories (all P<.01). These findings show differences between children who triggered the warning and children who did not, providing a reference for identifying critically ill children for targeted care.

PMID:42155091 | DOI:10.2196/89306