Category: Nevin Manimala

BMC Health Serv Res. 2026 Aug 7;26(1):1068. doi: 10.1186/s12913-026-15334-z.

ABSTRACT

OBJECTIVE: This study aimed to assess sociodemographic characteristics, health-seeking behaviors, and breast cancer screening beliefs among women aged 18-69 years attending Family Health Centers (FHCs) in Ankara, Türkiye, and to determine predictors of participation in mammography screening. It is design as a descriptive, cross-sectional study.

METHODS: Between April 2024 and January 2025, data were collected from 718 women using a sociodemographic questionnaire and the Turkish-adapted Breast Cancer Screening Beliefs Scale (originally developed by Kwok et al., validated by Türkoğlu et al.). Descriptive analyses, Chi-square tests, and binary logistic regression were performed with IBM SPSS 25.0. Variables with p < 0.20 in univariate analyses were entered into multivariate modeling, and statistical significance was defined as p < 0.05.

RESULTS: Statistically significant differences were found between age groups in terms of marital status, education level, housing situation, presence of chronic disease, general health perception, and history of non-breast cancer screening (p < 0.001). According to the logistic regression analysis, women who are aware of screening programs are 8.50 times more likely to have a mammogram, those who have undergone non-breast cancer screening are 5.52 times more likely, those with chronic diseases are 1.98 times more likely, and those who describe their health perception as “good/very good” are 1.90 times more likely. (Nagelkerke R² = 0.465).

CONCLUSION: Mammography participation is influenced by both structural and personal determinants. Enhancing awareness, strengthening primary-care education, and addressing financial barriers could substantially improve screening uptake and facilitate earlier breast cancer diagnosis among Turkish women.

PMID:42571012 | DOI:10.1186/s12913-026-15334-z

BMC Health Serv Res. 2026 Aug 8;26(1):1067. doi: 10.1186/s12913-026-15289-1.

ABSTRACT

BACKGROUND: Methodological and technical education of nurses is required to address challenges of technology integration in peritoneal dialysis (PD). We analysed the nature of therapy interrupting system errors in automated peritoneal dialysis (APD) cyclers of a German PD reference care centre and provide applicable resolution proposals.

METHODS: We documented therapy interrupting system errors of HomeChoice Pro and HomeChoice Claria cyclers (Baxter, BX, Unterschleißheim, Germany) including the years 2015-2023. We systematically characterise and suggest resolution strategies to these errors based on routinely collected health data and user experience to safely guide nephrology professionals and patients during troubleshooting. The ‘reporting of studies conducted using observational routinely collected health data’ (RECORD)-statement was considered.

RESULTS: We identified 49 different therapy interrupting system errors which were assigned to eight major categories: Errors associated to the pressure chamber (N=18), the Digitalboard/EEPROM (N=18, Electrically Erasable Programmable Read Only Memory), air in set (N=3), opening of the set’s door (N=3), those associated to the energy system (N=3), the heater system (N=3) and a variety of errors resulting in an abrupt termination or non-commencement of the treatment session without clear group assignment (undefined, N=1) or without sufficient documentation. In total, 138 error incidents were documented during the observational timeframe. In frequential order errors were attributed to the pressure chamber (21.74%,), and air in set (19.57%) where specifically error 2240 occurred in 17.39% of total cases, the Digitalboard/EEPROM (17.39%), the energy or battery (16.67%; No. 1032 in 19/23 cases), the set’s door (7.25%) and the heater (4.35%). In 17 cases (12.32%) no error number was documented by the patient or staff. One error cause was registered as undefined (0.72%). Overall, errors were rarely occurring in <0.25% of treatment sessions performed in the observational timeframe.

CONCLUSION: These findings may be useful to guide training purposes for nephrology personnel and persons doing APD. For the manufacturer, the information provided may be helpful to improve error management and information transmission to the persons doing APD with the HomeChoice devices.

PMID:42571003 | DOI:10.1186/s12913-026-15289-1

Ann Surg Oncol. 2026 Aug 8. doi: 10.1245/s10434-026-20381-1. Online ahead of print.

ABSTRACT

BACKGROUND: The 2025 American thyroid association (ATA) guidelines introduced a revised risk stratification system for thyroid cancer incorporating histology-specific models and a four-tier classification framework. However, its clinical performance in patients with papillary thyroid carcinoma (PTC) remains insufficiently validated. This study compared the 2015 and 2025 ATA risk stratification systems for predicting PTC recurrence and evaluated reclassification patterns.

PATIENTS AND METHODS: This retrospective cohort study included 284 patients who underwent surgery for PTC at Samsung Medical Center, Seoul, Korea, between 2019 and 2021. Risk classification was performed according to the 2015 ATA three-tier system (low, intermediate, and high risk) and the 2025 ATA four-tier system (low, low-intermediate, intermediate-high, and high risk). Predictive performance for recurrence was assessed using disease-free survival (DFS) and the integrated area under the receiver operating characteristic curve (iAUC).

RESULTS: During follow-up, structural recurrence occurred in 23 patients (8.1%). Under the 2015 system, recurrence occurred in 0/119 low-risk, 8/106 intermediate-risk, and 15/59 high-risk patients. Under the 2025 system, recurrence occurred in 0/86 low-risk, 1/56 low-intermediate-risk, 2/53 intermediate-high-risk, and 20/89 high-risk patients, with recurrence events predominantly concentrated in the high-risk group. The 2025 ATA system resulted in substantial reclassification, particularly among patients initially classified as intermediate risk. The iAUC for predicting DFS was 0.873 for the 2015 system and 0.852 for the 2025 system.

CONCLUSIONS: The 2025 ATA risk stratification system demonstrated predictive performance comparable to the 2015 system while concentrating recurrence events predominantly within the high-risk group, suggesting improved risk discrimination in patients with PTC.

PMID:42570987 | DOI:10.1245/s10434-026-20381-1

Environ Sci Pollut Res Int. 2026 Aug 8. doi: 10.1007/s11356-026-38086-z. Online ahead of print.

ABSTRACT

Industrial oily wastewater and oil spills pose serious environmental threats, demanding efficient and scalable separation materials. In this work, a hydrophobic/oleophilic ZIF-8/polyurethane sponge (ZIF-8/PUS) composite was fabricated via a simple dip-coating and drying process at room temperature. The composite was characterized by X-ray diffraction, FTIR, scanning electron microscopy (SEM), Brunauer-Emmett-Teller (BET) surface area analysis, and Barrett-Joyner-Halenda (BJH) pore size distribution, confirming successful ZIF-8 deposition and a well-developed porous architecture. The water contact angle increased from 96.8 to 148.4°, while the oil contact angle remained 0°. The sponge achieved an oil absorption capacity of 36.1 g g⁻¹ for 15W-40 oil and a water flux of 107 L m⁻² h⁻¹. Temperature-dependent separation tests (25-85 °C) yielded COD removal efficiencies of 99.24-97.55% and oil and grease removal of 99.20-97.74%. Statistics on triple experiments validated these results’ robustness: One-way ANOVA showed significant differences in COD and oil/grease removal among experimental groups (p < 0.0001), and Tukey’s HSD test showed that optimally adjusted samples outperformed controls (p < 0.05). The sponge retained performance after ten absorption-squeezing cycles. Application to real drilling rig wastewater (RIG-69 Fath unit) achieved 94.97% COD removal (6689 to 67 mg L⁻¹) and 99.07% oil and grease removal, demonstrating practical potential for industrial oily wastewater treatment.

PMID:42570976 | DOI:10.1007/s11356-026-38086-z

Sci Rep. 2026 Aug 8;16(1):24542. doi: 10.1038/s41598-026-64405-y.

ABSTRACT

Cognitive-behavioral therapy (CBT) is the first-line treatment for obsessive-compulsive disorder (OCD), yet a significant number of patients do not achieve remission or substantial symptom relief. This study aims to enhance the prediction of CBT outcomes in OCD by integrating demographic, clinical, and neuroimaging data using machine learning (ML) models. We conduct a comprehensive analysis on a well-characterized clinical sample, employing a rigorous validation scheme to avoid data leakage, and comparing multiple ML algorithms to minimize bias. Out of four different ML models trained on demographic and clinical data, structural MRI, and resting-state MRI functional connectivity data, no model was able to predict CBT success significantly above chance level in the present sample. Although clinical and demographic data enabled 64%-66% accuracy for predicting remission, this did not reach statistical significance after permutation testing. Pre-treatment symptom severity emerged numerically as the most promising predictor of remission, aligning with previous studies, but did not pass the significance threshold in the present study. Despite efforts to identify neuroimaging predictors, neither functional nor structural MRI features significantly contributed to the prediction models. These findings suggest that robust, individualized brain-based predictions for mental health outcomes remain challenging with the available data and sample size.

PMID:42570963 | DOI:10.1038/s41598-026-64405-y

Nat Commun. 2026 Aug 8;17(1):8014. doi: 10.1038/s41467-026-76312-x.

ABSTRACT

Established determinants of health in the elderly help guide routines for indicated vaccine administration, while unmeasured frailty may limit vaccine access. We evaluate the performance of the 2024-2025 COVID-19 vaccine adapted to the Omicron JN.1 lineage in a Swedish population aged ≥65 years (N = 245 696). Vaccine effectiveness (VE) on COVID-19-related hospitalization and a negative control outcome (NCO; all-cause mortality) are assessed in various cohorts between October 1, 2024 to March 31, 2025. The VE was 75% (95% CI 70%-79%) overall, and 84% (95% CI 80%-87%) and 65% (95% CI 34%-82%) in individuals with and without vaccination with the prior updated COVID-19 vaccine in 2023-2024, respectively. The NCO in individuals exposed to the study vaccine in 2024-2025 was half of that seen in those not exposed to the vaccine (HR 0.43 [95% CI 0.40-0.45]). Removing individuals hospitalized with COVID-19 from this population did not change the difference in NCO (HR 0.43 [95% CI 0.41-0.46]). These findings suggest the presence of selection effects arising from under-provision of health services to elderly individuals with frailty. The healthy vaccinee effect should be considered in observational studies of the effectiveness of updated COVID-19 vaccines in elderly populations.

PMID:42570961 | DOI:10.1038/s41467-026-76312-x

Evid Based Dent. 2026 Aug 8. doi: 10.1038/s41432-026-01238-8. Online ahead of print.

ABSTRACT

OBJECTIVE: To evaluate the accuracy and reliability of three AI platforms ChatGPT, Perplexity, and Google Gemini in assessing the methodological quality of systematic reviews using the AMSTAR 2 checklist, compared with expert manual evaluation in dental research.

METHODS: A cross-sectional comparative study was conducted to assess the performance of three AI platforms ChatGPT, Perplexity, and Google Gemini in evaluating the methodological quality of 35 systematic reviews using the AMSTAR 2 checklist. Manual assessments by a domain expert served as the reference standard. Each AI system was prompted with a standardized AMSTAR 2 query, and item-level outputs were collected for direct comparison. Key metrics included percentage agreement, error proportions, and inter-rater reliability measured by Cohen’s kappa. Error proportions represent the proportion of discordant assessments out of total valid pairwise comparisons across 16 AMSTAR-2 items. Differences between LLM-generated and reference AMSTAR-2 ratings were summarized using effect estimates with corresponding 95% confidence intervals. Comparative performance across platforms was assessed based on confidence-interval overlap rather than hypothesis testing. Results are presented as effect estimates with corresponding 95% confidence intervals, without hypothesis testing or statistical dichotomization. This approach provided a robust and reproducible framework to benchmark AI-assisted quality appraisal in dental evidence synthesis.

RESULTS: Among 35 systematic reviews assessed, Perplexity demonstrated the highest agreement with expert AMSTAR-2 ratings (error proportion: 19.0%; weighted κ_w: 0.78, 95% CI 0.71-0.85), followed by ChatGPT (error proportion: 22.9%; weighted κ_w: 0.62, 95% CI 0.54-0.70) and Google Gemini (error proportion: 43.9%; weighted κ_w: 0.41, 95% CI 0.33-0.49). Perplexity also achieved the best sensitivity (81.3%, 95% CI 76.5-85.4%) and specificity (82.7%, 95% CI 78.1-86.5%) for correctly identifying high-quality reviews. Non-overlapping 95% confidence intervals suggest meaningful differences in performances among platforms, with Perplexity showing superior agreement across all metrics. Across all platforms, agreement was generally higher for non-critical AMSTAR-2 domains involving clear and structured reporting, whereas performance was weaker for critical domains requiring interpretation of complex methodological details, risk-of-bias considerations, and evidence synthesis procedures.

CONCLUSIONS: Perplexity demonstrated the highest accuracy and agreement with expert assessments of the methodological quality of systematic reviews, suggesting its potential as a supportive AI tool for AMSTAR-2-based appraisal in dental evidence synthesis. In contrast, systematic biases observed in ChatGPT and Google Gemini underscore the continued need for human oversight to ensure the validity of methodological assessments. Differences in agreement and error proportions were observed across all models when compared with expert AMSTAR-2 evaluations, indicating meaningful variability in methodological appraisal performance, reinforcing that AI-assisted appraisal of systematic review methodology should complement rather than replace expert human judgment in dental research.

PMID:42570960 | DOI:10.1038/s41432-026-01238-8

Sci Rep. 2026 Aug 8;16(1):24551. doi: 10.1038/s41598-026-62965-7.

ABSTRACT

During the COVID-19 pandemic, inter-hospital transfer of critically ill patients between intensive care units was widely used to manage capacity shortages. While several individual studies have compared outcomes of transferred and non-transferred patients, no systematic synthesis of this evidence exists. This systematic review aims to determine whether inter-hospital ICU-to-ICU transfer of adult COVID-19 patients is associated with increased mortality or other adverse clinical outcomes. We systematically searched PubMed, Web of Science, and Scopus for observational studies comparing clinical outcomes of adult COVID-19 patients who underwent inter-hospital ICU-to-ICU transfer with non-transferred ICU patients. Two reviewers screened, selected, and extracted data independently and in duplicate. Risk of bias was assessed using the Newcastle-Ottawa Scale and ROBINS-I, and certainty of evidence using GRADE. Exploratory random-effects meta-analyses were performed separately for each effect measure, with Hartung-Knapp and crude-effect sensitivity analyses. Nine observational studies from seven countries were included (approximately 6,100 transferred and 29,200 non-transferred ICU patients). None of the adjusted effect estimates showed a statistically significant mortality disadvantage for transferred patients. Pooled adjusted subgroup estimates were an odds ratio of 1.29 (95% CI 0.84 to 1.98; k = 3) and a hazard ratio of 0.71 (95% CI 0.34 to 1.46; k = 2). Length of stay was longer in transferred patients in most studies. The available observational evidence did not show a clear increase in mortality among transferred compared with non-transferred COVID-19 ICU patients. These findings support the use of inter-hospital transfer as a safe strategy for managing ICU surge capacity. PROSPERO CRD420261356915.

PMID:42570952 | DOI:10.1038/s41598-026-62965-7

Ultrasound Med Biol. 2026 Aug 8:S0301-5629(26)00287-5. doi: 10.1016/j.ultrasmedbio.2026.07.013. Online ahead of print.

ABSTRACT

OBJECTIVE: To investigate the value of Microvascular Flow (MV-Flow) vascular pattern and quantitative parameter vascular index (VI) in differentiating benign from malignant lymph nodes.

METHODS: A total of ninety-five patients who underwent ultrasound-guided lymph node aspiration biopsy at the First Hospital of Sun Yat-sen University between February 2023 and September 2023 were prospectively enrolled. Conventional ultrasound and MV-Flow examinations were performed, and the diagnostic efficacy was analyzed. The influencing factors of the VI were investigated.

RESULTS: Among grayscale features, lymph node hilum, shape, liquefaction, and calcification differed significantly between benign and malignant nodes (p < 0.05). In the vascular pattern of Color Doppler Flow Imaging (CDFI) and MV-Flow, benign nodes mainly showed hilar flow, while malignant nodes mainly showed mixed flow (p < 0.05 for both). MV-Flow and CDFI demonstrated comparable overall diagnostic performance (AUC: 0.682 vs. 0.646), with no statistically significant difference (p > 0.05). MV-Flow vascular pattern classification showed good interobserver agreement (κ = 0.801), and the vascular index (VI) demonstrated high reliability (ICC = 0.811). A moderate negative correlation was observed between lymph node depth and VI across all four sections.

CONCLUSIONS: MV-Flow provided superior qualitative visualization of microvascular flow compared to CDFI, but this did not translate into a statistically significant improvement in quantitative diagnostic performance for differentiating benign from malignant lymph nodes. Therefore, it should serve as a secondary diagnostic adjunct for lymph nodes with indeterminate CDFI findings, particularly to microvascular architecture that traditional Doppler fails to detect.

PMID:42570926 | DOI:10.1016/j.ultrasmedbio.2026.07.013

Acad Radiol. 2026 Aug 8:S1076-6332(26)00557-X. doi: 10.1016/j.acra.2026.07.049. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: To compare an integrated “Plaque-Vessel” high-resolution vessel wall imaging (HR-VWI) radiomics model against a “Plaque-only” model for predicting 1-year adverse clinical events in symptomatic intracranial atherosclerotic stenosis (ICAS).

MATERIALS AND METHODS: This retrospective study enrolled 272 symptomatic ICAS patients. A temporal split was applied: patients from 2022 (n = 133) constituted the training and validation cohorts, while those from 2023 (n = 139) served as an independent temporal test cohort. Radiomic features were extracted from T1-weighted and contrast-enhanced T1-weighted HR-VWI. Two signatures were constructed: a “Plaque” model (plaque ROI only) and a “Plaque-Vessel” model (incorporating plaque, vessel wall, and lumen). The primary endpoint was a composite of 1-year adverse clinical events. Performance was assessed using the area under the curve (AUC) and decision curve analysis (DCA), comparing against a qualitative imaging model (comprising intraplaque hemorrhage, plaque enhancement, and positive remodeling), the Essen Stroke Risk Score (ESRS), and NIHSS.

RESULTS: The Plaque-Vessel model yielded the highest predictive performance, achieving an AUC of 0.800 (95% CI: 0.724-0.863) in the temporal test set. This performance showed a trend toward improvement compared to the Plaque-only model (AUC = 0.721, P = .27) and significantly outperformed the qualitative plaque features model (AUC = 0.631, P = .03), ESRS (AUC = 0.554, P < .01), and NIHSS (AUC = 0.543, P < .01). Calibration curves showed good agreement. DCA demonstrated that the Plaque-Vessel signature provided greater net clinical benefit than the Plaque-only model or clinical scores across the majority of threshold probabilities.

CONCLUSION: The integrated Plaque-Vessel radiomics model significantly outperforms conventional clinical scores and routine visual plaque assessment for predicting 1-year adverse events. However, given the inconclusive statistical difference between the integrated and plaque-only models, these findings remain exploratory. The model requires further large-scale validation before it can be considered ready to guide routine clinical decisions.

PMID:42570909 | DOI:10.1016/j.acra.2026.07.049