Search Results (3,842)

Background/Objectives: The Diet Quality Questionnaire (DQQ) is a brief, food group–based instrument designed for globally comparable population surveillance of diet quality. We culturally adapted the DQQ for Poland and evaluated its internal structure and reliability in an adult cohort. Methods: Following forward–backward translation and expert review, the Polish DQQ was administered online to adult females. Internal structure was explored and test–retest reliability was assessed for total DQQ scores. Diet quality indicators (Dietary Diversity Score [DDS], NCD-protect, NCD-risk, and Global Dietary Recommendations score [GDR]) were summarized descriptively. Results: The average age in the cohort was 29.4 ± 13.6 years. A total of 296 respondents completed the survey; 100 completed the retest. Item-level test–retest reliability was good to excellent (Cohen’s kappa 0.72–1.00). Agreement for total scores was high with minimal bias (Bland–Altman bias 0.2, >95% of observations within limits of agreement) and there was no heteroscedasticity; Passing–Bablok regression indicated equivalence between the test and retest. Median (IQR) diet quality indicators were: DDS 6.0 (5.0; 7.0), NCD-protect 2.5 (1.5; 4.0), NCD-risk 2.5 (1.0; 4.0), and GDR 9.0 (7.5; 10.5). Eighty percent met DDS ≥ 5, while one-third consumed all five recommended food groups. Conclusions: DQQ-PL demonstrates high item-level stability and strong agreement for total scores, with structural findings aligning with its design as a non-latent, food group checklist for population monitoring. The Polish adaptation is feasible and reliable in the studied population (young adult women), supporting its potential use for rapid dietary surveillance pending broader validation. Full article

Background: Peripheral nerve stimulation (PNS) is increasingly used in selected patients with neuropathic pain, and many individuals seek supplemental online information to clarify procedural expectations and postoperative care. Large language models such as ChatGPT may provide scalable patient education; however, their performance for PNS-related questions has not been evaluated. This study assessed the reliability, accuracy, and comprehensibility of ChatGPT-5.0 responses to common PNS patient questions. Methods: We conducted a cross-sectional evaluation of ChatGPT-5.0 responses to 21 standardized questions derived through expert consensus, spanning pre-implantation, implantation, and post-implantation domains. Sixteen board-certified interventional pain specialists and a nurse educator independently rated each response using validated scales for reliability (1–6), accuracy (1–3), and comprehensibility (1–3). Descriptive statistics were calculated, and domain-level patterns were examined. Results: Clinician ratings demonstrated generally strong performance across all domains. Mean reliability was 4.7 ± 1.4, mean accuracy 2.6 ± 0.6, and mean comprehensibility 2.8 ± 0.5. Foundational questions addressing mechanisms, expectations, and postoperative care received the highest ratings. Lower ratings were observed for implantation-focused items requiring procedural nuance. No response fell below predefined acceptability thresholds, and sensitivity analyses confirmed that including one partial evaluator did not alter the observed trends. Conclusions: ChatGPT-5.0 generated responses to PNS-related patient questions that clinicians rated as generally reliable, accurate, and understandable, particularly for foundational and postoperative topics. Performance was more variable for procedural questions, underscoring the need for clinician oversight and verification. These findings provide a benchmark of current LLM capabilities and highlight the importance of ongoing evaluation as models evolve and as patients access versions with differing functionalities. Full article

Purpose: Opioids are widely used for pain management but are associated with adverse events that may differ between women and men. However, post-marketing safety data are rarely examined at the individual level to characterize these sex differences. This study aimed to identify sex disparities in opioid-associated adverse events using the FDA Adverse Event Reporting System (FAERS) to inform safer opioid selection for women. Methods: Opioid drugs were identified using the FDA’s Opioid Analgesic Risk Evaluation and Mitigation Strategy (REMS) list and official drug labeling. Relevant FAERS reports were extracted, and adverse events were classified into 27 System Organ Classes (SOCs) based on the Medical Dictionary for Regulatory Activities (MedDRA). Sex-specific signals of disproportionate reporting were evaluated using proportional reporting ratios and reporting odds ratios for drug–SOC pairs. Results: Across most opioid drugs and SOCs, adverse events were reported more frequently in women than in men. The largest sex disparities were observed for codeine, fentanyl, tapentadol, hydrocodone, and sufentanil, with significantly higher disproportionate reporting rates among women. These findings indicate pronounced sex-specific differences in the post-marketing safety profiles of several commonly used opioids. Conclusions: Women demonstrate higher disproportionate reporting of adverse events for certain opioid medications, particularly codeine and fentanyl. These results suggest the need for increased awareness of sex-specific safety differences and support sex-informed prescribing and monitoring strategies to improve opioid safety in women. Since pharmacists are medication experts and play a key role in promoting rational and safe use, our findings may further support pharmacists counseling patients and monitoring for opioid-related adverse events. Full article

Background/Objectives: This study aimed to automatically classify Zirconia-based fixed restorations and porcelain-fused-to-metal (PFM) restorations on panoramic radiographs using an artificial intelligence-based model. Unlike previous studies that mainly focused on classifying types of restorations (e.g., crowns, fillings, implants), this research concentrated on material-based differentiation, aiming to provide a more specific contribution to clinical decision support systems. Method: Panoramic radiographs obtained from the archive of Süleyman Demirel University Faculty of Dentistry were included in this study. Radiographs with poor image quality or insufficient visibility of the restoration area were excluded. A total of 593 cropped region-of-interest (ROI) images, labeled by expert prosthodontists using ImageJ software (version 1.54r; National Institutes of Health, Bethesda, MD, USA), were included in the analysis. In order to reduce class imbalance, data augmentation was applied only for images in the Zirconia-based fixed restorations class. By using various image processing techniques such as rotation, reflection and brightness change, the number of samples in the zirconia-based restorations class was increased and thus a balanced dataset was obtained with a close number of samples for both classes. For model training, the pre-trained VGG16 architecture was used with a transfer learning method, and the final layers were retrained and fine-tuned. The model was configured specifically for binary classification. The entire dataset was randomly split into 70% training, 20% validation, and 10% testing. Model performance was evaluated using accuracy, F1-score, sensitivity, and specificity. Results: The model correctly classified 90 out of 94 images in the test dataset, achieving an overall accuracy rate of 96%. For both classes, the precision, recall, and F1-score values were measured in the range of 95% to 96%. Additionally, the Area Under the Curve (AUC) of the ROC curve was calculated as 0.994, and the Average Precision (AP) score was determined to be 0.995. According to the confusion matrix results, only 4 images were misclassified, consisting of 2 false positives and 2 false negatives. Conclusions: The deep learning model demonstrated high accuracy in differentiating zirconia and metal-supported porcelain restorations on panoramic radiographs, suggesting that material-based AI classification may support clinical decision-making in restorative dentistry. Full article

Background: Maternal control and the sense of support significantly influence a woman’s experience of birth. This study aimed to adapt and validate the Support and Control in Birth (SCIB) scale in Spanish women to assess maternal perceptions of support and control during birth, and to develop and validate an abbreviated version of the instrument. Methods: A cross-sectional study was conducted with a sample of 302 Spanish women who had given birth within the previous 6 months and were at least 1 week postpartum. Content, construct, and criterion validity, as well as reliability, were analysed using an expert panel, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Cronbach’s Alpha Coefficient, and Intraclass Correlation Coefficient (ICC). Criterion validity was assessed using the Generalised Anxiety Disorder Screener (GAD-7) and the Birth Satisfaction Scale–Revised (BSS-R). Results: The KMO test yielded a value of 0.925, and Bartlett’s test of sphericity was significant (p < 0.001). EFA identified three factors (Support, External control, and Internal control) that explained 56.49% of the total variance. CFA showed good model fit for most of the evaluated indices. The SCIB scale correlated negatively with the GAD-7 and positively with the BSS-R (p < 0.001), as well as with several obstetric and neonatal variables (p < 0.05): planned pregnancy, high-risk pregnancy, onset and type of delivery, birth plan, use of epidural analgesia, maternal involvement, postpartum complications, and newborn characteristics. Cronbach’s alpha was 0.951, and the ICC indicated excellent consistency and agreement (0.995; 95% CI: 0.990–0.998). Based on expert panel consensus, a 24-item abbreviated version was developed that exhibited psychometric properties similar to those of the original version and a high correlation with it (r > 0.90). Conclusions: The Support and Control in Birth (SCIB) scale is a valid and reliable instrument for assessing perceptions of support and control during birth in Spanish women. The 24-item abbreviated version is recommended. Full article

Background/Objectives: Radiomics-based machine learning models have demonstrated high accuracy in differentiating benign from malignant orbital masses, with early studies suggesting performance comparable to expert radiologists. However, translation into clinical practice remains limited due to dataset constraints, including retrospective study designs, single-center cohorts, and underrepresentation of diverse patient populations. This review aims to evaluate the current evidence supporting radiomics in orbital disease while critically examining barriers to generalizability and equity across ophthalmology, otolaryngology, and plastic surgery. Methods: A narrative literature review was conducted to assess radiomics applications in orbital oncology and reconstruction. Studies evaluating diagnostic accuracy, margin assessment, postoperative surveillance, and surgical planning across ophthalmology, head and neck surgery, and reconstructive surgery were analyzed, with particular attention paid to dataset composition, validation strategies, and imaging standardization. Results: Radiomics models demonstrated high diagnostic performance in differentiating orbital tumors, optimizing surgical planning, and aiding postoperative monitoring. However, most studies relied on small, homogeneous datasets lacking racial, ethnic, and pediatric representation. External validation was uncommon, and imaging heterogeneity limited reproducibility. These deficiencies restrict the clinical translation of radiomics and risk exacerbating healthcare disparities, particularly among underrepresented populations. Conclusions: Radiomics holds promise as a precision medicine tool for orbital diagnosis, surgical navigation, and postoperative care. Nevertheless, its clinical adoption is constrained by dataset bias, lack of standardization, and limited prospective validation. Future progress requires multi-institutional, demographically diverse datasets and standardized imaging protocols to ensure equitable and generalizable implementation across specialties. Full article

Decision Transformers (DTs) reformulate reinforcement learning as a conditional sequence modeling problem and have demonstrated competitive performance in offline Reinforcement Learning (RL) scenarios. However, their behavior in card games, specifically partially observable imperfect-information, trick-taking games remains underexplored. In parallel, general-purpose card-game toolkits have shown the value of unified environments and standardized evaluation protocols for accelerating research in imperfect-information games. Motivated by the goal of creating a general card-game-playing framework, we present a unified RL pipeline for trick-taking card games using DTs. While classical learning methods have demonstrated strong performance in card games, transformer-based reinforcement learning remains comparatively underexplored in this domain. This paper studies the applicability of DTs to the core play-phase of trick-taking games and evaluates whether a single, reusable pipeline can be transferred across multiple games in this class with minimal game-specific engineering. We propose a unified framework integrating offline pretraining, online selective expert iteration, and inference-time legal-action filtering. Crucially, our proposed approach demonstrates two key advantages over standard implementations. First, the model successfully internalizes complex game rules (e.g., follow-suit constraints) implicitly from the empirical data distribution, completely eliminating the need for explicit action masking during training. Second, we introduce a selective expert iteration mechanism equipped with strict acceptance filtering, which effectively prevents distribution collapse and enables safe, monotonic offline-to-online policy refinement. Ultimately, we show that this single, reusable transformer-based pipeline achieves competitive performance across multiple trick-taking domains (Hearts, Whist, and Spades) with minimal game-specific engineering. Full article

Background/Objectives: In exercise science and sports medicine, the potential use of large language models for generating personalized exercise programs is being explored. However, the practical applicability of AI-generated exercise prescriptions has not yet been sufficiently validated, particularly in complex clinical contexts. This study aimed to evaluate their practical utility under expert supervision. Methods: Exercise prescription outputs generated by a large language model (Gemini 2.5, Google LLC) were analyzed using clinical cases incorporating complex exercise-related considerations. Three levels of prompt structuring were applied. Experts evaluated the outputs using a structured rubric assessing safety, feasibility, guideline alignment, and personalization. Inter-expert agreement was assessed using intraclass correlation coefficients (ICC), and expert-specific internal consistency was evaluated using Cronbach’s alpha. Results: AI-generated exercise prescriptions demonstrated a certain level of structural completeness. However, inter-expert agreement was low (ICC (2,3) = 0.139), whereas expert-specific internal consistency was high (Cronbach’s alpha > 0.92). Prompt structuring from Stage 1 to Stage 2 was associated with improved mean scores in safety and guideline alignment. Additional structuring did not consistently yield further improvements. Conclusions: AI-generated exercise prescriptions may have practical potential as supportive decision-making tools when expert involvement is assumed. Nonetheless, expert judgments did not converge toward a single evaluative standard, reflecting the inherently expert-dependent nature of exercise prescription. Full article

Background: Breakpoints in the pre-examination processes and at organizational interfaces are a significant source of failures in specimen identification and tracking in anatomic pathology. While ISO 15189 emphasizes end-to-end traceability and risk-based quality management, implementing these principles in complex, multi-actor specimen pathways remains challenging. This study explores expert perspectives on specimen process chains, tracking mechanisms, and ISO 15189-oriented quality and risk management in pathology. Methods: We conducted 10 semi-structured expert interviews across three settings. Interviews were audio-recorded, transcribed, pseudonymized, and analyzed using structured qualitative content analysis (Mayring) supported by MAXQDA. A deductive category system derived from the theoretical framework and interview guide comprised six main categories and twelve subcategories. Results: Across 512 coded text segments, participants identified several factors as critical for effective implementation, including: (i) interface management along the specimen pathway, with recurrent vulnerabilities at handovers between operating theater/ward/transport and accessioning; (ii) the central role of barcode-based identification and the need for closed-loop traceability; (iii) the importance of measurable quality indicators and incident learning systems to operationalize risk management; (iv) persistent paper–digital handoffs and heterogeneous IT landscapes that undermine data integrity; (v) the need for clearly assigned responsibilities, training, and SOP governance; and (vi) implementation barriers including resources, change management, and vendor integration, alongside practical enablers such as incremental roll-out and cross-professional governance. Conclusions: Experts converge on a pragmatic ISO 15189-aligned roadmap: prioritize interface risks, standardize identifiers and handover rules, define a minimal KPI set for tracking and misidentification events, and reduce paper–digital handoffs by interoperable IT. Future work should quantify baseline error rates and evaluate the impact of digital tracking interventions on patient safety and turnaround times. Full article

Background: Medication use in paediatric populations is inherently complex and carries a heightened risk of prescribing errors, particularly within primary health-care settings. Despite this concern, evidence describing paediatric prescribing errors in Saudi Arabia remains scarce. Hence, the present study aimed to evaluate the prevalence and patterns of prescribing errors in paediatric primary care and to characterize the pharmacist-led interventions undertaken to resolve these errors. Methods: A prospective, mixed-methods cross-sectional study was conducted over three months at a primary health-care centre. Paediatric outpatient prescriptions were systematically reviewed during routine practice by trained clinical pharmacists. All suspected errors were independently validated and classified for severity by a multidisciplinary expert panel. Descriptive statistics were used to summarise prescribing errors, and associations with patient and prescription characteristics were assessed using chi-square tests. Qualitative data were analysed using a descriptive thematic approach to explore mechanisms of error identification and the nature of corrective pharmacist interventions. Results: A total of 545 paediatric outpatient prescriptions were reviewed, of which 142 prescriptions (26.1%) contained at least one prescribing error. Across these prescriptions, a total of 145 individual prescribing errors were identified. Dose-related errors were the most common (68.3%), followed by inaccuracies in dosing frequency (11.0%) and inappropriate drug selection (9.0%). The occurrence of prescribing errors was significantly associated with patient weight (p = 0.016), the number of medications per prescription (p < 0.001), and the recorded diagnosis (p = 0.018). The majority of errors were intercepted prior to medication dispensing (93.0%), and no cases of patient harm were identified. Qualitative analysis revealed that errors were predominantly detected through cross-checking with authoritative drug references, recalculation of weight-based doses, and application of clinical judgement, and were most often resolved through direct communication with the prescribing clinician. Conclusions: Prescribing errors occur frequently in paediatric outpatient settings; however, most are preventable with appropriate safeguards. Pharmacists play a critical role in identifying and resolving these errors before they result in patient harm. Enhancing paediatric prescribing support systems and strengthening interprofessional collaboration may further advance medication safety within primary health-care services. Full article

Background: Hypertrophic cardiomyopathy (HCM) is a common inherited disease and a leading known cause of sudden cardiac arrest in young adults and athletes. While genetic testing has advanced rapidly in the past decade, the yield of genetic testing remains low. The Clinical Genome Resource (ClinGen) initiative has become a leading resource for defining the clinical relevance of genetic variants with expert groups focusing on evaluating the strength of evidence for each HCM implicated gene. With the rise of large biobanks and population databases, genetic discovery has been significantly advanced. However, whether these databases can be used to validate gene–disease associations curated by ClinGen and provide evidence for novel gene–disease associations remains unclear. Objectives: Here, we utilized a publicly available database containing 748,879 individuals across three large biobanks (All of Us, UK biobank, Mass General Brigham biobank). Methods: We tested the association of rare coding variants in each gene in the HCM ClinGen panel with HCM. In total, 38 genes were tested, and Bonferroni correction was applied accordingly. Results: Of the 12 genes with definitive evidence for HCM (e.g., MYBPC3, MYH7, TNNT2, ALPK3), 8 (67%) demonstrated nominally significant association with HCM on a population level, and 5 (42%) remained significant after Bonferroni correction, further supporting the validity of these genes in HCM panels. Several definitive genes which are much less commonly affected in HCM (CSRP3, MYL3, ACTC1, TPM1, FHOD3, MYL2, and TNNC1) did not pass our Bonferroni corrected-significance threshold, but all had positively associated effect sizes with HCM. No genes deemed to have moderate or limited evidence had any significant associations with HCM even before Bonferroni correction. Conclusions: Altogether, we show that large biobanks and population databases generally recapitulate established gene–disease associations for HCM and support the ClinGen group’s gene curations. The utilization of such publicly accessible databases represents an additional tool for assessing gene validity in monogenic cardiac disorders with an established phenotype, although it may have limited sensitivity and should not be solely relied on. Full article

The growing urgency of resource-efficient construction in water-stressed and rapidly urbanizing regions necessitates integrated decision support frameworks that move beyond isolated sustainability metrics. This study operationalizes the water-energy nexus within building design evaluation by developing a structured hybrid multi-criteria decision-making (MCDM) framework tailored to the Indian construction context. Unlike conventional sustainability assessments that treat water and energy independently, the proposed approach integrates life cycle-based water consumption, operational and embodied energy demand, environmental impacts, economic feasibility, and project constraints within a unified analytical hierarchy. A Delphi-validated criterion structure comprising five main criteria and twenty sub-criteria is weighted using the Analytic Hierarchy Process (AHP), and ranked using the VIKOR compromise solution method. To strengthen methodological robustness, ranking outcomes are validated across three independent MCDM logics including TOPSIS, PROMETHEE, and COPRAS. The framework evaluates four representative building strategies aligned with Indian regulatory and certification systems (NBC, ECBC, IGBC/GRIHA, and net-zero water-energy design). Using expert-informed weights derived from a Delphi–AHP involving a panel of experienced practitioners, the VIKOR compromise ranking consistently identifies the net-zero alternative as the most favorable option within the evaluated framework. The results are therefore interpreted as an expert-informed assessment demonstrating the applicability of the proposed decision support methodology rather than as statistically generalizable priorities for the entire Indian construction sector. The study contributes by (i) embedding nexus-based resource interdependence into building-level MCDM modeling, (ii) enhancing transparency through explicit benefit-cost classification and decision matrix disclosure, and (iii) demonstrating ranking stability across multiple validation techniques. The proposed framework provides a transferable methodological approach that can be adapted to different regional contexts through locally derived expert inputs. Full article

Background and Objectives: Müllerian duct anomalies (MDAs) are congenital malformations of the female genital tract for which several classification systems have been proposed. The objective of this study is to estimate the interrater reliability of the American Fertility Society (AFS), European Society of Human Reproduction and Embryology/European Society for Gynaecological Endoscopy (ESHRE/ESGE), American Society for Reproductive Medicine (ASRM) and Congenital Uterine Malformation by Experts (CUME) classification systems for Müllerian duct anomalies. Materials and Methods: This retrospective cohort study was conducted at a tertiary care hospital and included 71 patients aged up to 45 years who were assessed for a Müllerian duct anomaly between January 2000 and April 2023. Pelvic MRI images were independently evaluated by four readers, followed by a consensus meeting. The primary outcome was interrater reliability (Krippendorff’s α), and the secondary outcomes were the proportions of indeterminate and unclassifiable cases after consensus meeting. Results: The interrater reliability for MDA diagnosis was very low for all the classification systems (AFS α 0.63, 95% CI [0.57, 0.67]; ASRM α 0.46, 95% CI [0.41, 0.52]; ESHRE/ESGE α 0.33, 95% CI [0.29, 0.38]; CUME α 0.57, 95% CI [0.45, 0.72]). After consensus meeting, the ESHRE/ESGE system had more indeterminate cases (9.9%) and the ASRM system had more unclassifiable cases (20.6%). Conclusions: All the classification systems for Müllerian duct anomalies had a very low interrater reliability, with more indeterminate cases in the ESHRE/ESGE system and more unclassifiable cases in the ASRM system. We present our recommendations for the improvement of each classification system. The ultimate goal of future research should be the development of a single uniform system integrating the best features of these systems and with clinically relevant cut-off values, considering patients’ reproductive outcomes. Full article

Background: Societal stigma toward eating disorders and obesity remains pervasive and is associated with psychological distress, maladaptive eating behaviors, reduced help-seeking, and barriers to care. Despite its documented impact, comprehensive and psychometrically robust instruments to assess stigma—particularly in Spanish-speaking populations—are scarce. This study aimed to develop and validate a multidimensional measure of societal stigma toward eating disorders and obesity in Spain, grounded in contemporary stigma frameworks. Methods: A cross-sectional observational study was conducted in a large community sample recruited online (N = 2121). An initial pool of stigma-related items was developed based on theoretical and empirical literature and refined through expert content validation. Psychometric evaluation included item screening, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), bifactor modeling, and reliability assessment. The sample was randomly split for EFA (n = 988) and CFA (n = 658). Associations between stigma scores and sociodemographic and experiential variables were examined. Results: The final 36-item instrument demonstrated excellent psychometric properties. Bifactor analyses supported an essentially unidimensional structure dominated by a strong general stigma factor, with secondary content-specific dimensions (e.g., legitimacy, personal responsibility, visibility, and treatment beliefs). The theory-driven bifactor model showed excellent fit (CFI = 0.991; TLI = 0.990; RMSEA = 0.024). The general factor exhibited high reliability (ω_h = 0.87). Higher stigma was observed among men, older participants, and individuals without personal or familial experience of eating disorders or obesity. Conclusions: This study provides a reliable and theoretically grounded instrument for assessing societal stigma toward eating disorders and obesity in Spain. The scale enables systematic research on stigma and offers a valuable tool for public health surveillance, intervention development, and evaluation of anti-stigma initiatives aimed at promoting compassionate and equitable care. Full article

Adaptive reuse has emerged to become a tool for implementing the understanding of sustainability in the domain of architectural conservation, as it encourages the continued usage of old buildings as means of reducing environmental impact, as well as preserving socio-cultural capital while generating economic income. However, in its practice, the decisions regarding granting meanings, interpretation, and preserving memories within adaptation processes are dominated by expert-driven approaches that inadequately incorporate stakeholder values or intangible heritage dimensions. To this end, this study aims to contribute to the current debate by adopting a participatory co-evaluation framework that integrates both authenticity perspectives and sustainability dimensions using Multi-Criteria Decision Analysis (MCDA) for evaluating adaptive reuse alternatives for an abandoned prefabricated wooden heritage building. Stakeholder priorities were drawn through a workshop and transformed into normalized weights using the Simos technique. Four design alternative typologies—namely, Continuity, Cultivation, Differential, and Optimization—were assessed and compared against 20 performance indicators across heritage, social, ecological, and economic criteria using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Indicator-level analyses and sensitivity tests (±10% and ±20% weight variations) were applied to confirm the robustness of rankings. The results from the best-performing alternative demonstrated the trade-offs between heritage authenticity and sustainability objectives, as well as demonstrating how combining participatory methods with quantitative evaluation can support evidence-based decision-making for adaptive reuse. The applied integrated framework helps bridge the gap between heritage theory and practice by combining authenticity, participation, and sustainability in one analytical approach, supporting evidence-based decisions for adaptive reuse. Full article