Nevin Manimala

PLoS One. 2025 Jul 15;20(7):e0326922. doi: 10.1371/journal.pone.0326922. eCollection 2025.

ABSTRACT

This study aimed to develop the Italian version of the Compassionate Engagement and Action Scales (CEAS) and examine its validity and reliability among Italian-speaking adults. A total of 374 (mean age = 23.11) Italian speaking participants took part in the study. All of them completed a questionnaire comprising the CEAS, together with measures of self-compassion, self-criticism, social support, empathy, well-being and general distress, used to estimate the scale’s convergent and criterion-related validity. Confirmatory Factor Analysis (CFA) revealed a satisfactory fit for a model in which three second-order factors (Self-compassion, Compassion for others and Compassion from others) were further articulated in two first-order factors (Engagement and Action). All the scales presented good reliability in terms of internal consistency. Correlations with measures of social support, empathy, self-compassion, self-criticism, well-being, and general distress indicated good convergent and criterion-related validity of the Italian version of the CEAS. Taken together, these results suggest that the CEAS can be properly used with Italian-speaking individuals in order to assess the three compassion flows in terms of both engagement and action.

PMID:40663573 | DOI:10.1371/journal.pone.0326922

PLOS Glob Public Health. 2025 Jul 15;5(7):e0004890. doi: 10.1371/journal.pgph.0004890. eCollection 2025.

ABSTRACT

Although the prevalence of childhood stunting has reduced in Bangladesh over time, it is still considered a major public health issue. While research has determined the risk factors for childhood stunting in Bangladesh, the factors that lead to reductions in stunting have received very little attention. Hence, we examined the factors contributing to the changes in childhood stunting over time in Bangladesh using a decomposition approach. In this study, data on childhood stunting of 41,013 under-5 children (U5C) were utilized from the Multiple Indicator Cluster Survey (MICS) 2012 and 2019, which are nation-wide cross-sectional surveys. Mixed-effect logistic regression analysis was adopted to identify the predictors of childhood stunting, and multivariate decomposition analysis was used to examine the factors contributing to the changes in childhood stunting over time. The prevalence of stunting declined from 41.9% in 2012 to 28.0% in 2019. Regression analysis showed that lower education of household head and mothers, older children, lower wealth status of households, unimproved sanitation facilities, and being urban residents were significant predictors of childhood stunting. The decomposition analysis revealed that around 86% of the overall decline in stunting resulted from the differences in the effect of independent variables. Furthermore, children’s age, maternal education, place of residence, and regions were significant factors contributing to the decline in childhood stunting prevalence over time based on both compositional and behavioral changes in these factors. Although childhood stunting has decreased in Bangladesh over time, the current prevalence remains high. Over 86% of the overall decline in stunting was due to the differences in the effect of independent variables. Interventions targeting children of mothers with lower education, infants, rural children, and children from households with lower wealth status and unimproved sanitation facilities may help to reduce the stunting prevalence in Bangladesh.

PMID:40663564 | DOI:10.1371/journal.pgph.0004890

PLoS One. 2025 Jul 15;20(7):e0328452. doi: 10.1371/journal.pone.0328452. eCollection 2025.

ABSTRACT

The seamless integration of shared bikes and metro systems promotes green and eco-friendly travel, yet the supply-demand imbalance of shared bikes around metro stations remains a critical challenge, making accurate demand prediction particularly crucial. Targeting metro-adjacent areas, this study proposes a method to identify shared bike trips connecting to metro usage, effectively filtering out approximately 24% of non-connecting travel records within the buffer zones. A predictive model integrating a Spatiotemporal Attention Graph Convolutional Network (STAGCN), Long Short-Term Memory (LSTM) network, and Informer is developed to forecast shared bike demand for metro connectivity. Specifically, the Informer model incorporates STAGCN to capture spatial correlations in bike demand and introduces an LSTM module to learn long- and short-term temporal dependencies. The final demand prediction is generated through a multilayer perceptron. Experiments conducted on shared bike and metro datasets in Shenzhen demonstrate that the proposed model achieves a coefficient of determination (R2) of 0.893, outperforming baseline models by 6.7% in prediction accuracy. Additionally, it exhibits lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) compared to traditional time-series forecasting methods. The proposed demand prediction model can assist operators in optimizing the allocation of shared bike resources, which is of great significance for improving user experience.

PMID:40663546 | DOI:10.1371/journal.pone.0328452

J Med Internet Res. 2025 Jul 15;27:e74299. doi: 10.2196/74299.

ABSTRACT

BACKGROUND: Perioperative education is crucial for optimizing outcomes in neuroendovascular procedures, where inadequate understanding can heighten patient anxiety and hinder care plan adherence. Current education models, reliant on traditional consultations and printed materials, often lack scalability and personalization. Artificial intelligence (AI)-powered chatbots have demonstrated efficacy in various health care contexts; however, their role in neuroendovascular perioperative support remains underexplored. Given the complexity of neuroendovascular procedures and the need for continuous, tailored patient education, AI chatbots have the potential to offer tailored perioperative guidance to improve patient education in this specialty.

OBJECTIVE: We aimed to develop, validate, and assess NeuroBot, an AI-driven system that uses large language models (LLMs) with retrieval-augmented generation to deliver timely, accurate, and evidence-based responses to patient inquiries in neurosurgery, ultimately improving the effectiveness of patient education.

METHODS: A mixed methods approach was used, consisting of 3 phases. In the first phase, internal validation, we compared the performance of Assistants API, ChatGPT, and Qwen by evaluating their responses to 306 bilingual neuroendovascular-related questions. The accuracy, relevance, and completeness of the responses were evaluated using a Likert scale; statistical analyses included ANOVA and paired t tests. In the second phase, external validation, 10 neurosurgical experts rated the responses generated by NeuroBot using the same evaluation metrics applied in the internal validation phase. The consistency of their ratings was measured using the intraclass correlation coefficient. Finally, in the third phase, a qualitative study was conducted through interviews with 18 health care providers, which helped identify key themes related to the NeuroBot’s usability and perceived benefits. Thematic analysis was performed using NVivo and interrater reliability was confirmed through Cohen κ.

RESULTS: The Assistants API outperformed both ChatGPT and Qwen, achieving a mean accuracy score of 5.28 out of 6 (95% CI 5.21-5.35), with a statistically significant result (P<.001). External expert ratings for NeuroBot demonstrated significant improvements, with scores of 5.70 out of 6 (95% CI 5.46-5.94) for accuracy, 5.58 out of 6 (95% CI 5.45-5.94) for relevance, and 2.70 out of 3 (95% CI 2.73-2.97) for completeness. Qualitative insights highlighted NeuroBot’s potential to reduce staff workload, enhance patient education, and deliver evidence-based responses.

CONCLUSIONS: NeuroBot, leveraging LLMs with the retrieval-augmented generation technique, demonstrates the potential of LLM-based chatbots in perioperative neuroendovascular care, offering scalable and continuous support. By integrating domain-specific knowledge, NeuroBot simplifies communication between professionals and patients while ensuring patients have 24-7 access to reliable, evidence-based information. Further refinement and research will enhance NeuroBot’s ability to foster patient-centered communication, optimize clinical outcomes, and advance AI-driven innovations in health care delivery.

PMID:40663377 | DOI:10.2196/74299

Br J Radiol. 2025 Jul 15:tqaf161. doi: 10.1093/bjr/tqaf161. Online ahead of print.

ABSTRACT

The TNM classification system is fundamental for describing the anatomical extent of lung cancer, encompassing the primary tumor (T), lymph node involvement (N), and distant metastases (M). It is crucial for patient stratification, treatment planning, and survival prognosis. Clinical staging (cTNM) relies on imaging and physical exams, while pathological staging (pTNM) uses surgical specimens. Advances in tumor biology, imaging, surgery, and treatments necessitate periodic updates to ensure the system reflects current knowledge and practices effectively. The International Association for the Study of Lung Cancer (IASLC), alongside the American Joint Committee on Cancer (AJCC) and Union for International Cancer Control (UICC), updates the system through a global, multidisciplinary approach supported by international data and statistical analysis. The ninth edition of the TNM classification, effective January 1, 2025, introduces revisions to the N and M categories while the T categories remain identical. N2 (ipsilateral mediastinal nodal disease) is now divided into N2a (single lymph node station involvement) and N2b (multiple N2 stations involvement). Similarly, M1c category is split into M1c1 (metastases confined to one organ system) and M1c2 (metastases involving multiple organ systems). These updates aim to improve the accuracy and utility of lung cancer staging in clinical practice and research.

PMID:40663374 | DOI:10.1093/bjr/tqaf161

JAMA Netw Open. 2025 Jul 1;8(7):e2518440. doi: 10.1001/jamanetworkopen.2025.18440.

ABSTRACT

IMPORTANCE: The PACIFIC trial established durvalumab as the standard-of-care therapy for unresectable, stage III non-small cell lung cancer (NSCLC) without progression following concurrent chemoradiotherapy (cCRT). Novel immunotherapy combinations involving the anti-CD73 monoclonal antibody oleclumab or the anti-NKG2A monoclonal antibody monalizumab have the potential to build on the durvalumab standard of care.

OBJECTIVE: To report updated results from the phase 2 COAST trial of consolidation durvalumab alone or combined with oleclumab or monalizumab in patients with unresectable, stage III NSCLC and no progression following cCRT.

DESIGN, SETTING, AND PARTICIPANTS: COAST was an open-label, phase 2, multidrug platform randomized clinical trial conducted across 73 sites globally. Patients with an Eastern Cooperative Oncology Group Performance Status of 0 or 1 and no progression following definitive platinum-based cCRT were enrolled between January 2019 and July 2020. The data cutoff for this final analysis was July 18, 2023. Data were analyzed from September 2023 to March 2024.

INTERVENTION: Patients were randomized 1:1:1, stratified by histologic type within 42 days after cCRT, to durvalumab alone or durvalumab combined with oleclumab or monalizumab for up to 12 months.

MAIN OUTCOMES AND MEASURES: The primary end point was investigator-assessed confirmed objective response rate (ORR). Key secondary end points included investigator-assessed progression-free survival (PFS), overall survival (OS), and safety. Efficacy end points were assessed in the intention-to-treat population. Safety was assessed in the as-treated population.

RESULTS: Of 189 randomized patients (median [range] age, 65 [37-87] years; 129 males [68.3%]; 176 [93.1%] current or former smokers), 186 received treatment consisting of durvalumab plus oleclumab (n = 59), durvalumab plus monalizumab (n = 61), or durvalumab alone (n = 66). Of these patients, 1 (0.5%) self-reported as American Indian or Alaska Native, 14 (7.5%) as Asian, 8 (4.3%) as Black or African American, 1 (0.5%) as Native Hawaiian or Other Pacific Islander, 159 (85.5%) as White, and 3 (1.6%) as other race. After a median (range) follow-up in all patients of 30.1 (0.4-48.9) months, confirmed ORR was numerically higher with durvalumab plus oleclumab (35.0%; 95% CI, 23.1%-48.4%) or monalizumab (40.3%; 95% CI, 28.1%-53.6%) than with durvalumab alone (23.9%; 95% CI, 14.3%-35.9%). However, the difference in ORR for durvalumab plus oleclumab (11.1 [-6.4 to 28.1] percentage points) and durvalumab plus monalizumab (16.9 [-0.8 to 33.4] percentage points) was not statistically significant compared with durvalumab alone. Both combinations prolonged PFS vs durvalumab alone (plus oleclumab: hazard ratio [HR], 0.59 [95% CI, 0.37-0.93]; plus monalizumab: HR, 0.63 [95% CI, 0.40-0.99]) but did not demonstrate nominal associations with longer OS (plus oleclumab: HR, 0.69 [95% CI, 0.40-1.20]; plus monalizumab: HR, 0.77 [95% CI, 0.44-1.33]). Safety was comparable across arms, without new or notable safety signals.

CONCLUSIONS AND RELEVANCE: In the COAST trial, combining consolidation durvalumab with oleclumab or monalizumab provided additional clinical benefit over durvalumab alone. This finding supports further investigation of these novel combinations in the phase 3 PACIFIC-9 trial.

TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03822351.

PMID:40663352 | DOI:10.1001/jamanetworkopen.2025.18440

JAMA Netw Open. 2025 Jul 1;8(7):e2521009. doi: 10.1001/jamanetworkopen.2025.21009.

ABSTRACT

IMPORTANCE: Examining racial and ethnic disparities in pediatric COVID-19 hospitalizations is critical to inform public health efforts to reduce those disparities.

OBJECTIVE: To characterize trends in pediatric COVID-19 hospitalizations by race and ethnicity from March 2020 to September 2023, focusing on recent epidemiologic findings (October 2022 to September 2023).

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used data from the COVID-19 Hospitalization Surveillance Network (COVID-NET) including 13 555 hospitalizations among patients aged 17 years or younger with a laboratory-confirmed SARS-CoV-2 infection who are residents of the COVID-NET catchment area in 12 states, covering approximately 10% of the US population.

EXPOSURE: Laboratory-confirmed SARS-CoV-2 infection within 14 days prior to or during hospitalization.

MAIN OUTCOMES AND MEASURES: Pediatric COVID-19-associated hospitalization rates by race and ethnicity and characteristics associated with COVID-19-associated hospitalizations.

RESULTS: Between March 2020 and September 2023, COVID-NET identified 13 555 pediatric hospitalizations (median patient age, 3.3 years [IQR, 0.6-12.5 years]; 7110 boys [52.5%]; 780 non-Hispanic Asian or Pacific Islander children [5.8%], 3837 non-Hispanic Black children [28.3%], 4131 Hispanic children [30.5%], and 4807 non-Hispanic White children [35.5%]). Hospitalization rates were 2.15 (95% CI, 2.01-2.34) times higher for Black children and 2.06 (95% CI, 1.91-2.23) times higher for Hispanic children compared with Asian or Pacific Islander children, who had the lowest rates. Despite overall decreased pediatric hospitalization rates between October 2022 and September 2023, higher rates of intensive care unit admissions among Black and Hispanic children persisted, at 1.88 (95% CI, 1.28-2.74) times higher for Black children and 2.13 (95% CI, 1.47-3.10) times higher for Hispanic children compared with Asian or Pacific Islander children. Among hospitalized children, 61.4% (95% CI, 57.0%-65.8%) of Black patients and 45.5% (95% CI, 41.9%-49.3%) of Hispanic patients had 1 or more underlying medical condition compared with 45.6% (95% CI, 42.1%-49.1%) of White children and 45.0% (95% CI, 41.9%-49.3%) of Asian or Pacific Islander children. Obesity (17.8%; 95% CI, 15.3%-20.5%) and neurologic disorders (15.2%; 95% CI, 13.7%-16.8%) were the most common conditions overall; 11.9% (95% CI, 9.1%-15.1%) of Black children had sickle cell disease, the fourth most common condition in this group.

CONCLUSIONS AND RELEVANCE: This study found that among pediatric patients hospitalized with COVID-19, Black and Hispanic children were disproportionately more likely to be hospitalized for COVID-19 and experience severe disease compared with White and Asian or Pacific Islander children. A higher proportion of hospitalized Black children had underlying medical conditions. This study underlines the need for targeted interventions, particularly for children with underlying medical conditions, and the need for equitable access and use of vaccines and therapeutics for disproportionately affected populations.

PMID:40663351 | DOI:10.1001/jamanetworkopen.2025.21009

JAMA Netw Open. 2025 Jul 1;8(7):e2521087. doi: 10.1001/jamanetworkopen.2025.21087.

ABSTRACT

IMPORTANCE: Trauma-focused treatments are effective for posttraumatic stress disorder (PTSD) but are rarely offered to patients with comorbid substance use disorder. Research suggests gender-based differences in prevalence and treatment needs for these patients, but treatment trials have mainly included men.

OBJECTIVE: To evaluate whether integrated trauma-focused psychological treatment (ie, integrated treatment) leads to greater reduction in PTSD symptom severity and weekly alcohol use than usual treatment (ie, relapse prevention) for alcohol use disorder (AUD) in women.

DESIGN, SETTING, AND PARTICIPANTS: This randomized clinical trial was conducted at 3 outpatient addiction services in Sweden. Data were collected from 2016 to 2021, and participants were followed up for 9 months after treatment initiation. Data were analyzed from October 2024 to April 2025. Participants were women older than 18 years with current PTSD and moderate-to-severe AUD diagnoses meeting Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria. Participants were randomly assigned to either the integrated treatment or relapse prevention arm. Intention-to-treat analyses were carried out using linear mixed models.

INTERVENTIONS: Twelve sessions, typically weekly, of integrated treatment (ie, Concurrent Treatment of PTSD and Substance Use Disorders Using Prolonged Exposure [COPE]) or relapse prevention were delivered by trained and experienced staff (including registered nurses, licensed psychologists, and social workers).

MAIN OUTCOMES AND MEASURES: Prespecified co-primary outcomes were PTSD symptom severity (assessed by blinded raters using Clinician-Administered PTSD Scale for DSM-5 [CAPS-5]) and weekly alcohol use (self-assessed using Timeline Followback) from baseline to the 9-month follow-up. Secondary outcomes included self-reported PTSD symptom severity, clinician-rated PTSD remission, and an objective biomarker of alcohol use (phosphatidylethanol level).

RESULTS: Ninety women (mean [SD] age, 44.7 [12.5] years) were included and randomly assigned to integrated treatment (n = 45) or relapse prevention (n = 45). In both arms, PTSD symptom severity decreased from baseline to 9-month follow-up (mean CAPS-5 score for integrated treatment: 37.40 [95% CI, 33.84-40.96] to 13.18 [95% CI, 8.95-17.41]; relapse prevention: 39.09 [95% CI, 35.53-42.65] to 23.68 [95% CI, 19.47-27.88]), with a significantly greater decrease in the integrated treatment arm than the relapse prevention arm (treatment-by-time interaction: F4,155 = 3.0; P = .02). Self-reported alcohol use decreased significantly over time (F14,581 = 3.0; P < .001) in both arms (integrated treatment: 144.41 [95% CI, 104.66-184.15] g/week to 92.65 [95% CI, 48.81-136.48] g/week; relapse prevention: 133.45 [95% CI, 93.71-173.19] g/week to 77.80 [95% CI, 31.65-123.95] g/week), but there was no detectable difference between treatments.

CONCLUSIONS AND RELEVANCE: In this trial of integrated treatment vs relapse prevention, integrated treatment led to a greater reduction in PTSD symptom severity and no detectable difference in alcohol use decrease compared with relapse prevention. These results support that integrated treatment can safely and effectively treat PTSD in women with AUD and ongoing alcohol use.

TRIAL REGISTRATION: ISRCTN.org Identifier: ISRCTN61391164.

PMID:40663349 | DOI:10.1001/jamanetworkopen.2025.21087

JAMA Netw Open. 2025 Jul 1;8(7):e2521101. doi: 10.1001/jamanetworkopen.2025.21101.

ABSTRACT

IMPORTANCE: Emergency department (ED) visits among patients with cancer reflect unmet needs and may indicate poor care quality. Outpatient palliative care interventions may help minimize unnecessary ED visits and improve care alignment with patient preferences.

OBJECTIVE: To explore characteristics of ED visits among patients referred to outpatient palliative care, evaluate the potential association of earlier referrals with reduced end-of-life ED visits, and examine when and where advance care planning was completed after outpatient palliative care referral.

DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study was conducted among patients with advanced cancer referred to outpatient palliative care at a tertiary hospital in South Korea, Seoul National University Hospital, between 2018 and 2022, with survival status confirmed as of June 25, 2023.

EXPOSURE: Referral to outpatient palliative care.

MAIN OUTCOMES AND MEASURES: End-of-life ED visits were defined as those occurring within a month before death. Factors associated with overall and end-of-life ED visits, as well as the completion of advance care planning documentation in outpatient palliative care and ED settings, were investigated.

RESULTS: Among 3560 patients with cancer (median [range] age, 68 [18-94] years; 2143 male [60.2%]), 920 patients (25.8%) visited the ED, with a total of 1395 visits. A total of 378 patients in the entire cohort (10.6%) had end-of-life ED visits, accounting for 474 visits. Most visits (958 visits [68.7%]) were related to cancer. Earlier palliative care referral was associated with more overall ED visits (odds ratio, 1.04; 95% CI, 1.02-1.06) but fewer end-of-life ED visits (odds ratio, 0.84; 95% CI, 0.80-0.89). Among 2132 patients who completed advance care planning documentation after referral to palliative care, 331 of 690 ED visitors (48.0%) and 761 of 1442 nonvisitors (52.8%) did so at the outpatient palliative care clinic, while 138 ED visitors (20.0%) completed it at the ED.

CONCLUSIONS AND RELEVANCE: This study found that a substantial number of patients with advanced cancer referred to outpatient palliative care visited the ED, including during the end-of-life phase. Earlier referrals were associated with fewer end-of-life ED visits, emphasizing the need for timely palliative care integration.

PMID:40663348 | DOI:10.1001/jamanetworkopen.2025.21101

High Blood Press Cardiovasc Prev. 2025 Jul 15. doi: 10.1007/s40292-025-00731-y. Online ahead of print.

ABSTRACT

INTRODUCTION: The teach-back method is an effective strategy for enhancing patient engagement in chronic disease management. However, no studies have explored the impact of combining this educational approach with wearable devices on patient engagement in hypertension management.

AIM: This study aimed to evaluate the effectiveness of a teach-back-based educational approach in promoting engagement with wearable devices among patients with hypertension.

METHODS: A longitudinal study was conducted. The study included 76 patients and included three phases. In the first phase (T0), patients received training about the use of two wearable technology devices through the teach-back method. Immediately afterward, participants completed the TWente Engagement with E-health Technologies Scale (TWEETS) to measure engagement at T0. Patient engagement was reassessed at 6 (T1) and 12 weeks (T2) after the educational training. A repeated-measures ANOVA was performed to compare the mean scores across the three phases.

RESULTS: The statistical analysis revealed no significant change in the TWEETS score between T0 and T1 (p = 0.42). However, the score significantly decreased at T2 compared to the previous two time points (p < 0.002).

CONCLUSIONS: Our results suggest that the teach-back method is effective for maintaining patient engagement in the short to medium term. However, its impact appears to decrease over time. Nurses can use the teach-back method to support education and enhance engagement with digital technology among hypertensive patients. Further research is needed to explore whether combining teach-back with additional strategies, such as gamification, telemedicine, remote monitoring, or peer support, can help sustain long-term patient engagement with digital health technologies.

PMID:40663315 | DOI:10.1007/s40292-025-00731-y