Category: Nevin Manimala

BMC Pregnancy Childbirth. 2025 Apr 14;25(1):442. doi: 10.1186/s12884-025-07574-4.

ABSTRACT

INTRODUCTION: Globally adverse birth outcome is being a series public health problem. As studies showed, even though the etiologies are multifactorial, extreme age pregnancies have more risk for adverse birth outcome. This study determines the estimated effect of teenage pregnancy on adverse birth outcome.

METHOD: The study analyzed data from the most recent Demographic and Health Surveys (DHS) data. Propensity score matching (PSM) analysis was employed by using age as treatment variable, teenager as treated and non-teenagers as control group and weighted sample of 45,790 (non-teenagers = 41,769 and teenagers = 4,021). The outcome variable; adverse birth outcome was categorized as “Yes” if a woman had either preterm birth, still birth, low birth weight or macrosomia in her recent birth and “No” otherwise. Covariates that had significant association with the treatment and outcome variables were considered for PSM analysis. After testing of each matching techniques (nearest neighbor, kernel and radius), the nearest neighbor (10) approach produced better covariate balance and selected as the best matching algorism for our analysis. Finally, the effect of teenage pregnancy on adverse birth outcome was measured and reported as average treatment effect on the treated (ATT) and the quality of matching and sensitivity to hidden bias was assed by t-statistics significance level and Mantel-Haenszel statistic respectively.

RESULTS: This study found that around one in ten (8.7%) of the women had pregnancy between the age of 15 and 19 years. The magnitude of adverse birth outcome among teenagers and non-teenagers was also 45.4% and 39.9% respectively. Teenage pregnancy contributed to a 4.7% increasing adverse birth outcome (ATT = 4.7%). Similarly, the Average Treatment Effect on Untreated (ATU) was 4.8%.

CONCLUSION: This study revealed that around one in teen women had pregnancy between the age of 15 and 19 years and teenage pregnancy had more risk of having adverse birth outcomes as compared to non-teenagers. Thus, we recommend to policy makers and implementers to design policies and strategies to improve teenagers’ access to prenatal care, family planning, and sexual education, awareness of creation on teenage pregnancy risks.

PMID:40229821 | DOI:10.1186/s12884-025-07574-4

BMC Med Inform Decis Mak. 2025 Apr 14;25(1):163. doi: 10.1186/s12911-025-02996-8.

ABSTRACT

BACKGROUND: The integration of artificial intelligence (AI) in healthcare has rapidly expanded, particularly in clinical decision-making. Large language models (LLMs) such as GPT-4 and GPT-3.5 have shown potential in various medical applications, including diagnostics and treatment planning. However, their efficacy in specialized fields like sports surgery and physiotherapy remains underexplored. This study aims to compare the performance of GPT-4 and GPT-3.5 in clinical decision-making within these domains using a structured assessment approach.

METHODS: This cross-sectional study included 56 professionals specializing in sports surgery and physiotherapy. Participants evaluated 10 standardized clinical scenarios generated by GPT-4 and GPT-3.5 using a 5-point Likert scale. The scenarios encompassed common musculoskeletal conditions, and assessments focused on diagnostic accuracy, treatment appropriateness, surgical technique detailing, and rehabilitation plan suitability. Data were collected anonymously via Google Forms. Statistical analysis included paired t-tests for direct model comparisons, one-way ANOVA to assess performance across multiple criteria, and Cronbach’s alpha to evaluate inter-rater reliability.

RESULTS: GPT-4 significantly outperformed GPT-3.5 across all evaluated criteria. Paired t-test results (t(55) = 10.45, p < 0.001) demonstrated that GPT-4 provided more accurate diagnoses, superior treatment plans, and more detailed surgical recommendations. ANOVA results confirmed the higher suitability of GPT-4 in treatment planning (F(1, 55) = 35.22, p < 0.001) and rehabilitation protocols (F(1, 55) = 32.10, p < 0.001). Cronbach’s alpha values indicated higher internal consistency for GPT-4 (α = 0.478) compared to GPT-3.5 (α = 0.234), reflecting more reliable performance.

CONCLUSIONS: GPT-4 demonstrates superior performance compared to GPT-3.5 in clinical decision-making for sports surgery and physiotherapy. These findings suggest that advanced AI models can aid in diagnostic accuracy, treatment planning, and rehabilitation strategies. However, AI should function as a decision-support tool rather than a substitute for expert clinical judgment. Future studies should explore the integration of AI into real-world clinical workflows, validate findings using larger datasets, and compare additional AI models beyond the GPT series.

PMID:40229819 | DOI:10.1186/s12911-025-02996-8

BMC Pulm Med. 2025 Apr 14;25(1):182. doi: 10.1186/s12890-025-03654-5.

ABSTRACT

BACKGROUND: Surfactant decreases surface tension in the peripheral airways and plays a role in regulating the lung’s immune responses. Several reports have documented changes in surfactant proteins levels, especially surfactant protein D (SP-D) and surfactant protein A (SP-A), suggesting their potential as biomarkers for asthma. However, the results of these studies are controversial. This systematic review was done to assess the levels of surfactant proteins in asthmatic patients compared to healthy individuals.

METHODS: A systematic review was conducted according to PRISMA guidelines. Searches were performed in the Medline/PubMed, Web of Science, Embase, and ScienceDirect databases to identify studies that assessed surfactants proteins levels in asthmatic patients. Pooled standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated using R version 4.4.3 meta package.

RESULTS: A total of 16 studies met the inclusion criteria and were thus considered for this systematic review. Among these, SP-D was the most frequently studied protein in relation to asthma, asthma severity, and lung function parameters in asthmatic patients. Serum and sputum levels of SP-D in asthmatic patients were slightly elevated compared to non-asthmatic individuals. However, these differences were not statistically significant; the pooled SMDs were 0.27 (95% CI: -0.034 to 0.574, P = 0.082) for serum levels and 1.47 (95% CI, -0.197 to 3.103, P = 0.084) for sputum levels. Similarly, no significant difference was detected for the analysis of serum SP-A levels, with SMD = 0.18 (95% CI, -0.505 to 0.866, P = 0.606). Though, some of the reviewed studies showed an association between SP-D levels and disease severity in asthmatic patients.

CONCLUSION: Although alterations have been observed in asthma and proposed as biomarkers, this systematic review did not find significant differences in the levels between asthmatics and healthy individuals. However, some studies have suggested an association between SP-D levels and asthma severity. Given the limited number of studies investigating this association, further research is needed to validate the clinical relevance of correlation between SP-D levels and asthma severity.

PMID:40229817 | DOI:10.1186/s12890-025-03654-5

Radiography (Lond). 2025 Apr 13;31(3):102948. doi: 10.1016/j.radi.2025.102948. Online ahead of print.

ABSTRACT

INTRODUCTION: Integrating decision support mechanisms utilising artificial intelligence (AI) into medical radiation practice introduces unique challenges to accountability for patient care outcomes. AI systems, often seen as “black boxes,” can obscure decision-making processes, raising concerns about practitioner responsibility, especially in adverse outcomes. This study examines how medical radiation practitioners perceive and attribute moral responsibility when interacting with AI-assisted decision-making tools.

METHODS: A cross-sectional online survey was conducted from September to December 2024, targeting international medical radiation practitioners. Participants were randomly assigned one of four profession-specific scenarios involving AI recommendations and patient outcomes. A 5-point Likert scale assessed the practitioner’s perceptions of moral responsibility, and the responses were analysed using descriptive statistics, Kruskal-Wallis tests, and ordinal regression. Demographic and contextual factors were also evaluated.

RESULTS: 649 radiographers, radiation therapists, nuclear medicine scientists, and sonographers provided complete responses. Most participants (49.8 %) had experience using AI in their current roles. Practitioners assigned higher moral responsibility to themselves in positive patient outcomes compared to negative ones (χ²(1) = 18.98, p < 0.001). Prior knowledge of AI ethics and professional discipline significantly influenced responsibility ratings. While practitioners generally accepted responsibility, 33 % also attributed shared responsibility to AI developers and institutions.

CONCLUSION: Patient outcomes significantly influence perceptions of moral responsibility, with a shift toward shared accountability in adverse scenarios. Prior knowledge of AI ethics is crucial in shaping these perceptions, highlighting the need for targeted education.

IMPLICATIONS FOR PRACTICE: Understanding practitioner perceptions of accountability is critical for developing ethical frameworks, training programs, and shared responsibility models that ensure the safe integration of AI into clinical practice. Robust regulatory structures are necessary to address the unique challenges of AI-assisted decision-making.

PMID:40228324 | DOI:10.1016/j.radi.2025.102948

Eur J Radiol. 2025 Apr 10;187:112106. doi: 10.1016/j.ejrad.2025.112106. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aims to investigate the value of a radiomics nomogram based on magnetic resonance imaging (MRI) in distinguishing vertebral compression fractures (VCFs) from vertebral osteomyelitis (VOs).

MATERIALS AND METHODS: We conducted a retrospective analysis of the clinical data from 100 patients with VCFs and VOs, respectively at our hospital. The cases were randomly divided into training (n = 140) and testing sets (n = 60) in a 7:3 ratio. Two experienced radiologists outlined the regions of interest (ROI) on the MRI images using T2-weighted fat suppression (T2WI-FS) images and extracted the radiomic features. The Least Absolute Shrinkage and Selection Operator (Lasso) algorithm was used to select and reduce radiomic features to establish a radiomics model (Model 1), and a Logistic Regression algorithm was used to construct a radiomics score. A multivariable logistic regression analysis was conducted to establish a clinical model (Model 2). A combined model (radiomics nomogram, Model 3) was built based on the radiomics score and independent clinical factors. The diagnostic performance of Models 1, 2, and 3 was validated using the Area Under the Curve (AUC) and Decision Curve Analysis (DCA).

RESULTS: The training and testing sets included 68/72 VCFs and 32/28 patients with VOs, respectively. There were no statistically significant differences in clinical characteristics such as age, sex, body mass index (BMI), CRP levels, ESR, and lesion stage between the training and testing sets (P > 0.05). A total of 873 radiomic features and 6 clinical features were extracted. After screening, 10 optimal features were selected to build Model 1, while 5 clinical features were used to build Model 2. Models 1 and 2 were combined to create Model 3 and a nomogram was plotted. All the three models were constructed using Logistic Regression algorithms. Model 3 achieved a higher AUC than Models 1 and 2 for both the training and testing sets: 0.946 > 0.904 > 0.871 (training) and 0.900 > 0.854 > 0.818 (testing), respectively. Additionally, the DCA indicated that Model 3 had better clinical utility than Models 1 and 2.

CONCLUSION: Our analysis indicated that the radiomics nomogram, combined with radiomic and clinical features, provides significant clinical guidance in distinguishing vertebral compression fractures from spinal vertebral osteomyelitis.

PMID:40228322 | DOI:10.1016/j.ejrad.2025.112106

J Cosmet Laser Ther. 2025 Apr 14:1-7. doi: 10.1080/14764172.2025.2468499. Online ahead of print.

ABSTRACT

Androgenetic alopecia significantly affects individuals’ quality of life, creating a need for better therapeutic strategies. Hair transplant surgery plays a key role in treating hair loss, with adjuncts like PRP and meso-cocktails explored to enhance results and procedure longevity. A prospective, single-blind study involved 112 males aged 25-50 years with Norwood-Hamilton grade IV-VI alopecia. Patients were randomly divided into two groups receiving different transplant methods. In Stage I, hair condition was evaluated, and QR678 Neo® was administered intradermally in Group A. Stage II involved hair transplantation surgery with diluted QR678 Neo® for graft preservation. Post-transplantation (Stage III) included additional QR678 Neo® administration in Group A. Evaluation methods consisted of global photographic assessment and videomicroscopy to measure hair growth and density. Group A (QR678 Neo®) demonstrated significant improvements in regrowth, density, and shaft diameter compared to Group B (conventional transplant). By Stage III, Group A had higher Global Photographic Assessment scores (8.87) and terminal hair count (181.02), with statistically significant differences (p < .001). The study suggests that QR678 Neo® improves outcomes and offers a promising treatment option for androgenetic alopecia.

PMID:40228316 | DOI:10.1080/14764172.2025.2468499

JMIR Rehabil Assist Technol. 2025 Apr 14;12:e66352. doi: 10.2196/66352.

ABSTRACT

BACKGROUND: Chronic pain in adolescents is a significant and growing concern, as it can have negative implications on physical and psychosocial development. Management can be complicated by the increasing risks associated with opioid misuse, highlighting the need for effective nonpharmacological interventions. Biofeedback is an empirically supported behavioral intervention for chronic pain that targets the self-regulation of physiological responses. Virtual reality (VR) is a novel delivery method for biofeedback that could serve as an engaging and effective platform for adolescents.

OBJECTIVE: The goal of this study was to assess the feasibility, acceptability, and preliminary effectiveness of integrating a VR-delivered respiratory biofeedback intervention into an outpatient pediatric pain rehabilitation program (PPRP) for adolescents with chronic pain.

METHODS: In this pilot study, we recruited 9 participants from those enrolled in the PPRP at Nemours Children’s Hospital. Participants underwent 2 VR respiratory biofeedback sessions per week over a 4-week period using AppliedVR’s “RelieVRx” program. Feasibility was defined as >60% of eligible patients enrolling with at least 80% of VR sessions completed. Acceptability was assessed via validated acceptability questionnaires, with high acceptability defined as an average acceptability rating score >3 on a 5-point Likert scale. Open-ended responses were analyzed via qualitative analysis. Preliminary effectiveness was assessed with questionnaires measuring the quality of life (Pediatric Quality of Life Inventory [PedsQL]) and level of pain interference in daily activities (Functional Disability Inventory) before and after participation in the pain program. Finally, heart rate (HR) and blood pressure (BP) were measured before and after each VR session.

RESULTS: Of 14 eligible PPRP patients, 9 (64%) enrolled in the VR respiratory biofeedback study, and 7 (77% of study participants) completed at least 80% of biofeedback sessions. Participants reported high acceptability with average session ratings ranging from 3.89 to 4.16 on post-VR program questionnaires. Of 224 open-ended responses, participants reported changes in stress and somatic symptoms (ie, pain distraction and breathing regulation). There was a statistically significant increase in the average physical functioning score of the PedsQL among participants (P=.01) from pre- to postparticipation in the overall pain program. The cohort’s average emotional functioning score of the PedsQL also increased, though this change was not statistically significant (P=.17). Participants’ Functional Disability Inventory scores significantly decreased from an average of 25.1 to 11 from before to after the pain program (P=.002). There were no significant differences between pre- versus post-BP or HR for any session. However, decreased BP and HR were observed across most sessions.

CONCLUSIONS: AppliedVR respiratory biofeedback demonstrated initial feasibility, acceptability, and preliminary effectiveness when implemented as part of a PRPP. This study underscores the need for future larger-scale studies analyzing the use of VR biofeedback in adolescent populations with chronic pain.

PMID:40228293 | DOI:10.2196/66352

JMIR Public Health Surveill. 2025 Apr 14;11:e66062. doi: 10.2196/66062.

ABSTRACT

BACKGROUND: GPFirst is a primary care partnership program designed to encourage patients with nonurgent conditions to seek care at participating general practitioner clinics instead of visiting the emergency department. In 2019, a digital media campaign (DMC) was launched to raise awareness and knowledge about GPFirst among residents in eastern Singapore.

OBJECTIVE: This study aims to assess the DMC’s impact on awareness and knowledge of GPFirst across different age groups, and the acceptability and satisfaction of GPFirst.

METHODS: The DMC, comprising Facebook posts and a website designed using the Andersen behavioral model, was evaluated through 2 repeated cross-sectional surveys. The first cross-sectional survey (CS1) was conducted with eastern Singapore residents aged 21 years and older, 2 1 year before the campaign’s launch, and the second survey (CS2) 4 months after. Satisfaction was measured on a 5-point Likert scale (very poor to excellent) about GPFirst experiences. Acceptability was assessed with 3 yes or no questions on decisions to visit or recommend GPFirst clinics. Analyses used tests of proportions, adjusted multiregression models, and age-stratified secondary analyses.

RESULTS: The Facebook posts generated 38,404 engagements within 5 months, with “#ThankYourGP” posts being the most viewed (n=24,602) and engaged (n=2618). Overall, 1191 and 1161 participants completed CS1 and CS2 respectively. Compared to CS1, CS2 participants were more aware (odds ratio [OR] 2.64, 95% CI 2.11-3.31; P<.001) and knowledgeable of GPFirst (OR 4.20, 95% CI 2.62-6.73; P<.001). Awareness was higher among married individuals (OR 1.31, 95% CI 1.04-1.66; P=.03), those without a regular primary care physician (OR 1.79, 95% CI 1.44-2.22; P<.001), and with higher education levels. Similarly, knowledge was greater among individuals with secondary (OR 2.88, 95% CI 1.35-6.17; P=.006) and preuniversity education (OR 2.56, 95% CI 1.14-5.70; P=.02), and those without a regular primary care physician (OR 1.54, 95% CI 1.02-2.34; P=.04). For acceptability, among participants who visited a GPFirst clinic, 98.2% (163/166) reported they would continue to visit a GPFirst clinic before the emergency department in the future, 95.2% (158/166) would recommend the clinic, 60.2% (100/166) cited the clinic’s participation in GPFirst as a factor in their provider’s choice and 87.3% (145/166) were satisfied with GPFirst. Among those unaware of GPFirst, 88.3% (1680/1903) would consider visiting a GPFirst clinic before the emergency department in the future.

CONCLUSIONS: The DMC improved awareness and knowledge of GPFirst, with high satisfaction and acceptability among participants. Age-dependent strategies may improve GPFirst participation. The “#ThankYourGP” campaign demonstrated the potential of user-generated content to boost social media engagement, a strategy that international health systems could adopt.

PMID:40228291 | DOI:10.2196/66062

JMIR Form Res. 2025 Apr 14;9:e66301. doi: 10.2196/66301.

ABSTRACT

BACKGROUND: Approximately 20% of US adults identify as having a mental illness. Structural and other barriers prevent many people from receiving mental health services. Digital mental health apps that provide 24-hour, real-time access to human support may improve access to mental health services. However, information is needed regarding how and why people engage with licensed counselors through a digital, real-time, text-based mental health support app in nonexperimental settings.

OBJECTIVE: This study aimed to evaluate how people engage with Counslr, a 24-hour, digital, mental health support app where users communicate in real time with human counselors through text messaging. Specifically, access patterns (eg, day of the week and time of session) and reasons for accessing the platform were examined. Furthermore, whether differences existed between session types (on-demand or scheduled) and membership types (education or noneducation) in regard to access patterns and why people accessed the platform were evaluated.

METHODS: The study population (users) consisted of students whose schools, universities, or colleges partnered with Counslr and employees whose organizations also partnered with Counslr. Users participated in text-based mental health support sessions. In these sessions, users engaged with licensed counselors through digital, text-based messaging in real time. Users could initiate an on-demand session or schedule a session 24 hours a day. User engagement patterns were evaluated through session length, session day, session time, and self-reported reasons for initiating the session. The data were stratified by membership type (education [students] or noneducation [employees]) and session type (on-demand or scheduled) to evaluate whether differences existed in usage patterns and self-reported reasons for initiating sessions by membership and session types.

RESULTS: Most students (178/283, 62.9%) and employees (28/44, 63.6%) accessed Counslr through on-demand sessions. The average and median session times were 40 (SD 15.3) and 45 minutes. On-demand sessions (37.9 minutes) were shorter (P=.001) than scheduled sessions (43.5 minutes). Most users (262/327, 80.1%) accessed Counslr between 7 PM and 5 AM. The hours that users accessed Counslr did not statistically differ by membership type (P=.19) or session type (P=.10). Primary self-reported reasons for accessing Counslr were relationship reasons, depression, and anxiety; however, users initiated sessions for a variety of reasons. Statistically significant differences existed between membership and session types (P<.05) for some of the reasons why people initiated sessions.

CONCLUSIONS: The novel findings of this study illustrate that real-time, digital mental health support apps, which offer people the opportunity to engage with licensed counselors outside of standard office hours for a variety of mental health conditions, may help address structural barriers to accessing mental health support services. Additional research is needed to evaluate the effectiveness of human-based apps such as Counslr and whether such apps can also address disparities in access to mental health support services among different demographic groups.

PMID:40228290 | DOI:10.2196/66301

Medicine (Baltimore). 2025 Apr 11;104(15):e41839. doi: 10.1097/MD.0000000000041839.

ABSTRACT

This study aimed to evaluate return to work (RTW) across different job groups, identify predictors of successful RTW, and investigate reasons for RTW failure. This case-control study, conducted in 2022, included 164 male patients who had ST elevation myocardial infarction (STEMI) in 2016 to 2017 and were registered in the 5-year ST Elevation Myocardial Infarction Cohort in Isfahan, Iran. Patients were divided into RTW (n = 82) and RTW failure (n = 82) groups, frequency-matched for education, marital status, and comorbidities. Baseline data were extracted from the cohort database, and occupational factors were gathered via telephone contact. Statistical analysis was performed using chi-square tests, t tests, and multivariate logistic regression to identify significant predictors of RTW, with P < .05 considered statistically significant. Data from 164 patients aged 18 to 65 with STEMI showed that those who returned to work had a mean age of 49.05 years, compared to 53.04 years for those who did not (P = .001). Factors associated with increased RTW included younger age (odds ratios [OR]: 0.86; 95% confidence intervals: 0.77-0.95), shorter hospitalization (OR: 0.63; 0.44-0.91), and lower first systolic blood pressure (OR: 0.97; 0.94-0.99). Most patients (49.39%, n = 59) returned within 1 month. Common RTW failure reasons were personal decisions (36.58%, n = 30), retirement (25.61%, n = 21), and choosing lighter jobs (25.61%, n = 21). No significant relationship was found between job groups of the International Standard Classification of Occupations and RTW (P = .581). Our study identifies key factors influencing RTW after STEMI, including age, history of myocardial infarction, hospitalization duration, treatment methods, and initial systolic blood pressure. The most common barrier to RTW was patient unwillingness. A comprehensive approach that integrates primary prevention, personalized rehabilitation, and financial and social support is recommended to improve RTW outcomes.

PMID:40228286 | DOI:10.1097/MD.0000000000041839