Browsing by Author "Cakir, Gokce Naz"

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The Effect of 3d Modeling on Family Quality of Life, Surgical Success, and Patient Outcomes in Congenital Heart Diseases: Objectives and Design of a Randomized Controlled Trial
(Turkish J Pediatrics, 2024) Sumengen, Aylin Akca; Ismailoglu, Abdul Veli; Ismailoglu, Pelin; Gumus, Terman; Celiker, Alpay; Namlisesli, Deniz; Cakir, Gokce Naz; Subaşi, Damla Özçevik
Background. Understanding the severity of the disease from the parents' perspective can lead to better patient outcomes, improving both the child's health -related quality of life and the family's quality of life. The implementation of 3 -dimensional (3D) modeling technology in care is critical from a translational science perspective. Aim. The purpose of this study is to determine the effect of 3D modeling on family quality of life, surgical success, and patient outcomes in congenital heart diseases. Additionally, we aim to identify challenges and potential solutions related to this innovative technology. Methods. The study is a two -group pretest -posttest randomized controlled trial protocol. The sample size is 15 in the experimental group and 15 in the control group. The experimental group's heart models will be made from their own computed tomography (CT) images and printed using a 3D printer. The experimental group will receive surgical simulation and preoperative parent education with their 3D heart model. The control group will receive the same parent education using the standard anatomical model. Both groups will complete the Sociodemographic Information Form, the Surgical Simulation Evaluation Form - Part I -II, and the Pediatric Quality of Life Inventory (PedsQL) Family Impacts Module. The primary outcome of the research is the average PedsQL Family Impacts Module score. Secondary outcome measurement includes surgical success and patient outcomes. Separate analyses will be conducted for each outcome and compared between the intervention and control groups. Conclusions. Anomalies that can be clearly understood by parents according to the actual size and dimensions of the child's heart will affect the preoperative preparation of the surgical procedure and the recovery rate in the postoperative period.
Evaluating Artificial Intelligence–Generated Nursing Care Plans: A Scenario-Based Comparative Study of Accuracy, Completeness, Quality, and Readability
(Wiley, 2026) Basut, Elif Aylin; Konyar, Mukaddes; Eden, Arzu Baygul; Akyaz, Dilek Yilmaz; Baygul Eden, Arzu; Yilmaz Akyaz, Dilek; Cakir, Gokce Naz; Tufekci, Seyma; Esim, Deniz
Aim This study aimed to evaluate the ability of three generative artificial intelligence tools (ChatGPT, Gemini and DeepSeek) to generate clinically accurate, comprehensive, and readable nursing care plans aligned with standardised nursing taxonomies (North American Nursing Diagnosis Association International, Nursing Interventions Classification, and Nursing Outcomes Classification). The study further explored variations in tool performance across different nursing specialties.Design A descriptive comparative design was used.Methods Ten expert-validated clinical scenarios representing five nursing specialties (Fundamentals of Nursing, Medical, Surgical, Paediatric and Psychiatric Nursing) were presented to the three artificial intelligence tools. Each tool responded to four standardised prompts based on the latest North American Nursing Diagnosis Association International, Nursing Interventions Classification and Nursing Outcomes Classification taxonomies. Outputs were assessed for quality, accuracy, completeness and readability by expert evaluators using validated scales.Results All tools produced nursing care plans of moderate-to-high quality. DeepSeek demonstrated slightly higher accuracy and completeness compared with Gemini and ChatGPT. Surgical nursing scenarios yielded the highest performance, likely reflecting the more protocolised and pathway-driven nature of perioperative care. However, all outputs were incomplete and written at a college-level readability, limiting accessibility for clinical use.Conclusion Generative artificial intelligence tools can support the production of structured nursing care plans requiring expert review and adaptation, particularly in less standardised clinical domains, but their limitations in completeness and readability indicate they should be regarded only as preliminary drafts requiring expert review and adaptation.Impact The study examined whether generative artificial intelligence can reliably assist in creating nursing care plans. All tools performed moderately well, with DeepSeek showing slight advantages, but outputs were incomplete and difficult to read. Findings are relevant to clinical nurses, educators, healthcare managers and policymakers worldwide who are exploring artificial intelligence in nursing workflows.Reporting Method This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.Patient or Public Contribution This study did not include patient or public involvement in its design, conduct or reporting.