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“Hospital or Prison! Not Both” Perspectives and Experiences of Forensic Psychiatric Patients on Hospitals in Türkiye
No full textThis study examined how service users in Türkiye experience hospitalization in a high-security forensic psychiatric setting. A focus group with seven male patients was analyzed using Interpretive Phenomenological Analysis. Three overarching themes emerged: perception of place, reflecting the hospital as both prison and hospital; rebuilding relationships, highlighting strengthened family ties but limited peer interactions; and contributions and challenges of the hospital, addressing the benefits of regular treatment alongside dissatisfaction with strict rules, insufficient activities, and limited information. The findings highlight how institutional environments shape patients’ experiences and emphasize the need for patient-centered, transparent, and rehabilitative forensic psychiatric care
Abiraterone Acetate Triggers ER Stress-Mediated Androgen Receptor Suppression via PERK/ATF4/CHOP Signaling in Prostate Cancer
No full text“ASSOCIATION of ANATOMICAL LOCATION WITH OUTCOMES AFTER MICROWAVE ABLATION TREATMENT of PARATHYROID ADENOMAS”
No full textTurkish Adaptation of the Family Stigma Stress Scale for Caregivers of Individuals with Neurodegenerative Diseases: A Validity and Reliability Study
No full textDetection of Mycobacterium tuberculosis in Ziehl–Neelsen Stained Sputum Smear Specimens Using Deep Learning Techniques
No full textABSTRACTThe initial step in diagnosing tuberculosis involves the microscopic examination of sputum samples using acid-fast staining to identify bacilli. However, this conventional method is labor-intensive, requires specialized expertise, is susceptible to errors, and has limited sensitivity. Research literature indicates that deep learning models demonstrate significant potential for detecting acid-fast bacilli (AFB) in sputum smear preparations. This study investigates the effectiveness of deep learning methods in identifying AFB within sputum smear samples. Our objective was to assess the performance of these models in tuberculosis diagnosis based on microscopic examination and to identify improvements they could bring in terms especially of sensitivity and availability within this field. We employed several transfer learning models: DenseNet201, ResNet101V2, Xception, InceptionResNetV2, and InceptionV3. In order to determine the effectiveness of these models, basic performance metrics such as accuracy, recall, precision, and F1 score were used. Among the transfer learning models we recommended, the InceptionV3 and Xception models exhibited the highest performance, achieving 99.00% high performance across all evaluation metrics. Our findings underscore that deep learning models can be effectively utilized for rapid and accurate detection of Mycobacterium tuberculosis in acid-fast stained sputum preparations.</div
Associations of ageism and social-emotional loneliness with aging outcomes among elderly individuals living in the community: a cross-sectional study
No full text06 Çoklu Basamak Tedavi Sonrası Pemetreksed ile Dramatik Yanıt Elde Edilen Primer Platin Refrakter Yüksek Dereceli Seröz Over Kanseri: Bir Kurtarma Tedavisi Başarısı
No full textTEKNOLOJININ KULLANIMI SAĞLIK ALANINDAKI AKADEMIK ÇALIŞMALARA NASILYÖN VERDI: DIJITAL SAĞLIK, MOBIL SAĞLIK, E-SAĞLIK, TELETIP VE YAPAY ZEKÂKONULU TEZLERIN BIBLIYOMETRIK ANALIZI
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Are answers obtained from artificial intelligence models for information purposes repeatable?
No full textIntroduction: The objective of this study was to assess the repeatability of orthodontic responses generated by multiple large language models across repeated time points. Methods: This experimental study assessed the answers provided by ChatGPT-3.5, ChatGPT-4.0, Gemini, and Gemini-Advanced to 40 frequently asked orthodontic questions. Each model was prompted with the same questions at three time points (T0: day 0, T1: day 7, and T2: day 14). Two blinded orthodontic experts independently evaluated responses using a 3-point accuracy scale. Cohen's Kappa and ICC were applied to assess inter-rater agreement and repeatability, respectively. In addition, Friedman test with Bonferroni post-hoc analysis and Spearman correlation were used for temporal comparisons. Results: Cohen's Kappa values between raters ranged from 0.624 to 0.749, indicating substantial inter-rater agreement. ICC values for repeatability ranged from 0.666 (Gemini) to 0.960 (ChatGPT-3.5). Friedman test results revealed significant differences in model accuracy at T0 and T2 (P < 0.001). Post-hoc analysis showed ChatGPT-3.5 differed significantly from Gemini and Gemini Advanced. Spearman correlations between time points were positive but weak (ρ = 0.284 to 0.383, P < 0.001). Conclusions: The study revealed statistically significant differences in repeatability among AI models. Despite high accuracy, some models exhibited limited consistency over time. These findings underscore the importance of evaluating both accuracy and temporal stability when integrating AI systems into clinical orthodontic communication