Tag: nevin manimala

Biomed Phys Eng Express. 2025 Nov 17. doi: 10.1088/2057-1976/ae202c. Online ahead of print.

ABSTRACT

Sleep arousal, characterized by emergence of light sleep or partial wakefulness, often indicates underlying physical disorders, and its detection is crucial for effective patient treatment. While the detection of arousals using multiple signals can be effective, the dependencies on multiple electrodes impose burden on patients. To resolve this issue, some effective features estimated from single-lead electroencephalography (EEG) signals were proposed to detect sleep arousal. Normalized and filtered EEG signals were segmented into 7-second frames, and scalograms were estimated using continuous wavelet transform (CWT). Scalograms and local properties such as frequency, bandwidth, band energy, band energy ratio, maxima, and regularity were derived from the coefficients of CWT. Final classification features were generated using statistical analyses. The most effective features, estimated by correlation coefficients and p-values, were subjected to an artificial neural network to evaluate the performance of the features. The maximum classification performances (86.72% accuracy, 89.26% sensitivity, 86.55% specificity, and 94.87% AUC) were achieved with 100 features. However, sixty specific features were selected from a total of 182 classification features, yielding nearly the same performance as the maximum. Finally, only 14 features were identified as making a pronounced contribution to arousal detection. These findings highlighted the potential of a feature-efficient single-channel EEG-based approach for reliable sleep arousal detection. The proposed framework can be integrated into patient monitoring systems, such as apnea detection modules, to provide a more comprehensive tool for sleep disorder management.

PMID:41248549 | DOI:10.1088/2057-1976/ae202c

J Educ Eval Health Prof. 2025;22:36. doi: 10.3352/jeehp.2025.22.36. Epub 2025 Nov 18.

ABSTRACT

PURPOSE: This study systematically evaluates and compares the performance of large language models (LLMs) in answering medical licensing examination questions. By conducting subgroup analyses based on language, question format, and model type, this meta-analysis aims to provide a comprehensive overview of LLM capabilities in medical education and clinical decision-making.

METHODS: This systematic review, registered in PROSPERO and following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, searched MEDLINE (PubMed), Scopus, and Web of Science for relevant articles published up to February 1, 2025. The search strategy included Medical Subject Headings (MeSH) terms and keywords related to (“ChatGPT” OR “GPT” OR “LLM variants”) AND (“medical licensing exam*” OR “medical exam*” OR “medical education” OR “radiology exam*”). Eligible studies evaluated LLM accuracy on medical licensing examination questions. Pooled accuracy was estimated using a random-effects model, with subgroup analyses by LLM type, language, and question format. Publication bias was assessed using Egger’s regression test.

RESULTS: This systematic review identified 2,404 studies. After removing duplicates and excluding irrelevant articles through title and abstract screening, 36 studies were included after full-text review. The pooled accuracy was 72% (95% confidence interval, 70.0% to 75.0%) with high heterogeneity (I2=99%, P<0.001). Among LLMs, GPT-4 achieved the highest accuracy (81%), followed by Bing (79%), Claude (74%), Gemini/Bard (70%), and GPT-3.5 (60%) (P=0.001). Performance differences across languages (range, 62% in Polish to 77% in German) were not statistically significant (P=0.170).

CONCLUSION: LLMs, particularly GPT-4, can match or exceed medical students’ examination performance and may serve as supportive educational tools. However, due to variability and the risk of errors, they should be used cautiously as complements rather than replacements for traditional learning methods.

PMID:41248547 | DOI:10.3352/jeehp.2025.22.36

J Public Health Manag Pract. 2026 Jan-Feb 01;32(1S Suppl 1):S122-S129. doi: 10.1097/PHH.0000000000002258. Epub 2025 Nov 18.

ABSTRACT

INTRODUCTION: Conceptualization of the COVID-19 response burden on the state and local government public health workforce is complicated as it continuously evolved and called upon nearly every aspect of the workforce. This study aims to catalog the scale of COVID-19 response among the state and local public health workforce by assessing the role of overtime in the delivery of public health services during pandemic response and shifts in agency size and program area distribution since the height of the pandemic.

METHODS: This study uses detailed state and local workforce survey data from the Public Health Workforce Interests and Needs Survey 2021 and 2024 administrations. The analytic sample includes the 72.2% of employees that served in a COVID-19 response role at any time during the pandemic (n = 30 914; N = 136 591) and 214 state and local agencies that participated in both years (n = 65 144 unduplicated responses).

RESULTS: In total, overtime equated for the equivalent of 25 000 FTE COVID-19 response employees, one-quarter (25%) of the total FTE COVID-19 response workforce. Of the 214 agencies that participated in both the Public Health Workforce Interests and Needs Survey 2021 and 2024, 41% of agencies (88) saw decreases in staff size overall between those 2 points in time. Shifts in primary program area between 2021 and 2024 are largely driven by changes in the proportion of non-full-time permanent employees.

CONCLUSION: This analysis magnifies the strain on the existing capacity of the public health workforce during the COVID-19 pandemic. These challenges stem from a chronically underfunded and understaffed workforce that was not prepared for surge capacity beyond existing employees. Given that many state and local public health agencies are smaller post-pandemic, it is reasonable to conclude that without large infrastructural changes, the workforce would likely face the same challenges if another pandemic-like crisis were to occur.

PMID:41248538 | DOI:10.1097/PHH.0000000000002258

J Public Health Manag Pract. 2026 Jan-Feb 01;32(1S Suppl 1):S60-S67. doi: 10.1097/PHH.0000000000002233. Epub 2025 Nov 18.

ABSTRACT

OBJECTIVE: Describe key characteristics of the rural local public health workforce on a national level, including in comparison to both the overall and urban local public health workforce.

DESIGN: Cross-sectional analysis of the 2024 Public Health Workforce Interests and Needs Survey (PH WINS) data.

SETTING: Local health departments (LHDs) serving rural and urban jurisdictions across the United States.

PARTICIPANTS: The study sample included 172 679 weighted responses from individuals working in LHDs, and 33 214 of them were from rural-serving LHDs.

MAIN OUTCOME MEASURES: Descriptive and bivariate statistics for measures across 4 areas, both overall and by rurality: demographic characteristics, educational background, position information, and intentions to stay or leave.

RESULTS: Greater portions of the rural local public health workforce were female and White relative to their urban counterparts. Compared to the urban workforce, the portions of the rural workforce without a public health degree and with clinical training were both greater. Tenure in position, agency, and public health practice also differed by rurality, with 19.6% of the rural workforce reporting the greatest tenure in public health practice (21 years or above) compared to 17.8% of the urban workforce. Intentions to stay, leave, or retire also differed by rurality, with 15.4% of the rural workforce reporting intentions to leave in the next year for reasons outside of retirement, compared to 21.6% of the urban workforce.

CONCLUSIONS: Characteristics of the local public health workforce vary by rurality, extending prior research demonstrating differences between rural- and urban-serving LHDs across the nation. Findings should guide rural-focused strategies aimed at strengthening and sustaining the public health workforce.

PMID:41248531 | DOI:10.1097/PHH.0000000000002233

J Public Health Manag Pract. 2026 Jan-Feb 01;32(1S Suppl 1):S49-S55. doi: 10.1097/PHH.0000000000002276. Epub 2025 Nov 18.

ABSTRACT

CONTEXT: Large local health departments (LHDs) serve diverse, high-need communities and are uniquely positioned to influence public health policy and practice locally, regionally, and nationally.

OBJECTIVE: The purpose of this study is to investigate policy engagement as a reported training need in the 2024 Public Health Workforce Interests and Needs Survey within large LHDs, highlighting gaps and opportunities to strengthen policy capacity.

DESIGN: Cross-sectional analysis of 2024 Public Health Workforce Interests and Needs Survey data using descriptive statistics and weighted logistic regression.

SETTING: Large LHDs, serving populations of 250 000 or more, across the US.

PARTICIPANTS: Study sample included 24 121 responses from individuals working in large LHDs.

MAIN OUTCOME MEASURES: Descriptive and regression-based statistics for training needs, self-identified skill-building interests, and predictors of reporting a policy engagement training need.

RESULTS: Nearly 40% of staff at large LHDs reported a training need in policy engagement, the only domain to show an increase in need since 2021. Women had significantly higher odds of reporting a policy training need (odds ratios [OR] = 1.67; P < .001), as did supervisors (OR = 2.09; P < .001) and managers (OR = 1.78; P < .001) compared to nonsupervisors, while those with master’s (OR = 0.64; P < .001) or doctoral degrees (OR = 0.40; P < .001) had lower odds compared to bachelor’s-level staff.

CONCLUSIONS: Large LHDs are well positioned to advance public health policy given their scale and connection to local communities. Targeting policy engagement training to workforce segments with the highest reported need offers a strategic opportunity to strengthen policy capacity across the US public health workforce.

PMID:41248529 | DOI:10.1097/PHH.0000000000002276

J Public Health Manag Pract. 2026 Jan-Feb 01;32(1S Suppl 1):S33-S44. doi: 10.1097/PHH.0000000000002248. Epub 2025 Nov 18.

ABSTRACT

CONTEXT: The Public Health Workforce Interests and Needs Survey (PH WINS) was fielded in 2014, 2017, and 2021. In the last 10 years, it has provided participating health departments and the field with data to improve recruitment and retention, strengthen workforce development efforts, guide strategic planning, and raise critical funds to improve public health infrastructure. It captures individual perspectives on engagement and satisfaction, intention to leave, training needs, and workplace infrastructure. This article describes the methods used for the 2024 administration of PH WINS.

PH WINS: PH WINS 2024 was fielded to a nationally representative sample of staff in State Health Agency Central Offices (SHA-CO) and local health departments (LHDs) from September 9, 2024, to January 17, 2025. The instrument was revised to improve the actionability of the results, reduce respondents’ cognitive burden, and align with existing standards or survey questions. PH WINS 2024 had 12 sampling frames, compared with the 3 in previous years: SHAs, members of the Big Cities Health Coalition (BCHC), and LHDs in each of the 10 Health and Human Services (HHS) Regions. All participating agencies were surveyed using a census approach.

PARTICIPATION: Overall, staff lists for 48 SHAs, 1,178 LHDs were collected, and the survey was sent to 159 627 individuals. PH WINS received a total of 56 595 responses, a 37% of eligible respondents. The SHA frame received responses from 29% of eligible respondents, BCHC members received 33%, and all other LHDs received 51%. The nationally representative SHA-CO frame included a total of 18 110 individuals, and the nationally representative LHD frame included 38 485 individuals from all 1178 LHDs. For the first time, the national sample of LHDs included small LHDs.

REFLECTIONS: With the 2024 administration of PH WINS, all state and local public health departments in the United States had the opportunity to participate, yielding a nationally representative sample of small LHDs for the first time. State and local health department leaders should be empowered to use the results for workforce development and other planning. Questions were modified to become more action-oriented, rigorous, and stable over time to maximize the utility of PH WINS for years to come. Given the changing public health landscape associated with new outbreaks, disasters, and the political environment, these changes are critical.

PMID:41248527 | DOI:10.1097/PHH.0000000000002248

J Med Internet Res. 2025 Nov 17;27:e67320. doi: 10.2196/67320.

ABSTRACT

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a chronic disease that requires management of blood glucose. According to previous studies, the Dario Digital Diabetes Solution (DDS) is a nonprescription digital health intervention with a smartphone app that has been shown to improve blood glucose control in adults with T2DM.

OBJECTIVE: This study aims to investigate the effects of DDS on health care resource use (HCRU) rates, charges, and estimated costs for adults with T2DM.

METHODS: In this retrospective cohort study, patient-level claims data of adults with T2DM were obtained from the Symphony Health Integrated Dataverse, a database containing both inpatient and outpatient claims, including diagnoses and procedures. Using exact and propensity score matching, DDS users and nonusers were matched in a 1:3 ratio. For the primary outcome measure (all-cause HCRU rates, defined as inpatient hospitalization and emergency room visits) and secondary outcome measures (all-cause outpatient visit rates, all-cause HCRU charges, and diabetes mellitus-related HCRU rates and charges), baseline, follow-up, and changes in values were summarized using descriptive statistics, and a multivariable generalized linear model or a 2-part model (including a generalized linear model) was applied. Additional exploratory outcome measures were analyzed. In a sensitivity analysis, a cost-to-charge ratio was calculated and applied to medical claims to estimate medical costs.

RESULTS: Following matching, cohorts consisted of 2445 DDS users and 7334 nonusers with similar demographic and baseline characteristics. The all-cause HCRU event rate was 9.3% lower in DDS users compared with nonusers at the 12-month follow-up from the index date. The mean number of events was estimated to be significantly lower in DDS users (0.48 per patient per year [PPPY]; 95% CI 0.44-0.52) than nonusers (0.52 PPPY; 95% CI 0.50-0.55), resulting in an incidence rate ratio of 0.91 (P=.04). Inpatient hospitalization was 23.5% lower in the DDS user cohort compared with the nonuser cohort, with emergency room visit and outpatient visit rates being similar across both cohorts. DDS users were numerically less likely to incur all-cause HCRU charges than nonusers (odds ratio 0.91, 95% CI 0.82-1.01; P=.07). All-cause HCRU charges were 26% lower for DDS users than for nonusers (US $ 12,552 PPPY savings; P<.001). When applying the cost-charge-ratio to the charges, the total estimated cost saving for DDS users was US $5077, of which US $4513 PPPY was attributed to all-cause HCRU and US $564 to all-cause office visits.

CONCLUSIONS: In this retrospective cohort study of adults in the United States with T2DM, DDS users were found to have lower all-cause HCRU rates than nonusers, driven by significantly lower inpatient hospitalization rates (P<.001). All-cause HCRU charges and estimated costs were shown to be lower for DDS users compared with nonusers.

PMID:41248488 | DOI:10.2196/67320

JCO Clin Cancer Inform. 2025 Nov;9:e2500140. doi: 10.1200/CCI-25-00140. Epub 2025 Nov 17.

ABSTRACT

PURPOSE: Direct pediatric information to inform chemotherapy emetogenicity in pediatric patients is limited. Therefore, the framework for antiemetic selection is uncertain. This study classified the acute emetogenicity of chemotherapy regimens in pediatric patients using data extracted from the electronic health record (EHR).

METHODS: This retrospective, single-institution study extracted data from the EHR of patients age 0 to 18 years who received chemotherapy during an inpatient admission from July 1, 2018, through February 29, 2024. Data were organized by patient and chemotherapy block including patient demographics; date, time, and route of chemotherapy and antiemetic administration; and date and time of vomiting. When at least 30 patients received the same chemotherapy and antiemetics during a chemotherapy block, the proportion of chemotherapy blocks where patients experienced complete, partial, or failed chemotherapy-induced vomiting control was determined. Chemotherapy regimen emetogenicity was assigned using a revision of an accepted pediatric chemotherapy emetogenicity classification framework that adjusted for antiemetic administration.

RESULTS: Seven thousand two hundred ninety-six chemotherapy blocks in 1,386 patients were identified. The emetogenicity of 25 chemotherapy regimens was classified: highly (7), moderately (5), low (10), and minimally (3) emetogenic. For 19 of these, no direct pediatric information was previously available. In five, our findings confirm the previous pediatric emetogenicity classification. Relative to emetogenicity classifications for adults, our findings led to classifications that were higher (seven regimens), lower (one regimen), or the same (four regimens).

CONCLUSION: We have applied a novel method, EHR data extraction, to provide direct pediatric evidence to classify chemotherapy emetogenicity. Increasing the certainty of chemotherapy emetogenicity facilitates effective antiemetic selection for pediatric patients. This method may be applied in multi-institution studies to increase the number of chemotherapy regimens whose emetogenicity is classified using direct pediatric evidence.

PMID:41248450 | DOI:10.1200/CCI-25-00140

J Craniofac Surg. 2025 Nov 17. doi: 10.1097/SCS.0000000000012172. Online ahead of print.

ABSTRACT

Secondary synostosis of initially patent cranial sutures is a rare but recognized phenomenon following cranial surgery, particularly in syndromic cases. However, its incidence and risk factors in nonsyndromic patients remain unclear. This study aimed to investigate the occurrence and causes of secondary synostosis following surgical correction of lambdoid synostosis, with a focus on posterior vault distraction. Medical records of patients who underwent surgical treatment for unilateral or bilateral lambdoid synostosis between 2015 and 2024 at Keio University Hospital were retrospectively reviewed. Patients with syndromic craniosynostosis were excluded. Postoperative cranial computed tomography at 1 year was assessed for secondary synostosis. Surgical approaches included single-stage cranial remodeling and posterior distraction. Statistical analyses were performed using the Mann-Whitney U and Fisher exact tests, with logistic regression applied to identify significant risk factors. Thirteen patients underwent surgery: 7 with bilateral lambdoid and sagittal synostosis (BLSS) and 6 with unilambdoid synostosis. 9 patients underwent single-stage cranial remodeling, and 4 underwent posterior distraction. Secondary coronal synostosis developed in all 4 patients who underwent posterior distraction but in none of those who underwent single-stage remodeling. Logistic regression analysis identified posterior distraction as the strongest risk factor for secondary coronal synostosis, while BLSS was associated with secondary sagittal synostosis. One patient required reoperation due to elevated intracranial pressure. Posterior vault distraction in nonsyndromic lambdoid synostosis is associated with a high risk of secondary coronal synostosis. These findings highlight the importance of careful long-term monitoring and judicious surgical planning, particularly when distraction is considered.

PMID:41248444 | DOI:10.1097/SCS.0000000000012172

J Craniofac Surg. 2025 Nov 17. doi: 10.1097/SCS.0000000000012132. Online ahead of print.

ABSTRACT

The large availability of high-resolution CT scans leads to the collection of vast amounts of 3D volumetric data on craniofacial malformations, as well as on normal patients of all ages. A precise analysis of deformities occurring in craniofacial malformations can be gained from comparison with a normal skull. Doing such a comparison with a single case may be considered a bias. We have utilized a function in the 3DSlicer application, known as DeCA (Dense Correspondence Analysis), which calculates a statistical mean 3D model from a set of models. From 184 CT scans, allegedly normal. We created 19 models named “Skull Atlas ###-###d” (###-### for the range of age in days). The statistical value of those models is verified using another function in 3Dslicer, named GPA, which draws equiprobable ellipses for the markups. We explain how to superpose the reference Skull Atlas with the skull of interest. We used the vestibular orientation for aligning the models.

PMID:41248433 | DOI:10.1097/SCS.0000000000012132