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    Do we need a guideline for all: a qualitative study on the experiences of male athletes following anterior cruciate ligament reconstruction
    (Bmc, 2024) Gokmen, Mehmet Yigit; Cepikkurt, Fatma; Belibagli, Mehmet Cenk; Uluoz, Mesut; Ozyol, Funda Coskun; Bavli, Ozhan; Turkmen, Mutlu
    BackgroundConsidering the low rate of qualitative studies on athletes with anterior cruciate ligament reconstruction (ACLR), aiming to access in-depth data, we thought that the utilization of the qualitative method would allow us to collect the appropriate and sufficient data to yield novel findings and achieve sound conclusions. The study's aim was to investigate anterior cruciate ligament (ACL) injury experience processes in athletes who had undergone isolated ACLR, reveal the clinically related milestones, and highlight the necessary gaps.MethodsSemi-structured interview techniques, in-depth follow-up questions, and thematic analysis were used to explore the experiences of participants with isolated ACL injuries 1-3 years after surgical treatment. The study was conducted in the Orthopaedics and Traumatology outpatient clinic of the Adana City Training and Research Hospital and included 14 male athletes who had undergone isolated primary ACLR. The study group's demographic and qualitative data were collected in the first week of September 2023. The member checking process was completed in the third following week. A thematic analysis checklist was used to ensure the reliability of the thematic analysis. The Consolidated Criteria for Reporting of Qualitative Research (COREQ) guidelines were followed.ResultsThe experiences of 14 patients (22.78 +/- 3.76 years, all males) were summarized into four themes that emerged from the data analysis process: 'The Distinctions in the Participants' Experiences Regarding the Moment of Injury,' 'Gathering Information about the ACL Injury,' 'Factors That Facilitate The Treatment Process and Reinforce Positive Experiences,' and 'Desperate Plight: Main Points of Patients' Negative Experiences.' Based on the main themes, there were 14 subthemes.ConclusionsOur study revealed that varying perceptions of ACL injury presented by the participants, which were caused by all stakeholders, including themselves, the professional environment, family members, social network, and the healthcare staff, showed that the physical and psychological impacts of the injury were observed in different severity levels at each stage of the process. We believe that an extensive guide for athletes with ACL injuries that includes all components of well-being and displays the required details for the sports club/coach, family/companion, and physician.
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    How accurately do large Language models interpret sport safeguarding principles: an evaluation using the International Olympic Committee framework
    (BMC, 2026) Gokmen, Mehmet Yigit; Belibagli, Mehmet Cenk; Karincaoglu, Ergin; Nazlican, Mahmut Cagatay; Turkmen, Eren; Yonal, Mehmet; Uluoz, Eren
    Background Safeguarding in sport, defined as the prevention of abuse, harassment, and exploitation, has become a core ethical responsibility for sport organizations. With the growing influence of artificial intelligence, large language models are increasingly being used to interpret, summarize, and operationalize policy documents. However, their reasoning fidelity in value-laden contexts such as athlete protection remains uncertain. Methods This document-based comparative study evaluated two advanced large language models, ChatGPT (OpenAI) and NotebookLM (Google), against twenty-five decision points derived from the 2024 International Olympic Committee Consensus on Interpersonal Violence and Safeguarding in Sport. Responses were independently scored by two evaluators across four dimensions: accuracy, ethical reasoning, applicability, and responsibility awareness. Quantitative and qualitative analyses assessed concordance with IOC recommendations, inter-rater reliability, and reasoning patterns. Structured PICO-style prompts were used to ensure standardized and comparable model interrogation across all decision points. Results ChatGPT achieved a higher mean composite score (3.84 +/- 0.41) than NotebookLM (3.32 +/- 0.77) (Wilcoxon Z = 3.41, p = 0.001, r = 0.68; n = 25 paired decision points), with full concordance in 21/25 (84.0%) versus 14/25 (56.0%) decision points. Inter-rater agreement was excellent (Cohen's kappa = 0.89). ChatGPT demonstrated stronger procedural reasoning and clearer alignment with IOC-defined responsibilities, while NotebookLM emphasized empathy and cultural nuance. Both models underperformed in survivor-centred reasoning and outcome evaluation metrics. Conclusion LLMs can partially reproduce the ethical and procedural logic embedded in sport safeguarding frameworks, but their reasoning remains incomplete without human oversight. Their integration into governance, education, and policy translation should follow clear ethical guardrails to prevent misinterpretation or amplification of harm. These systems may support policy drafting, education, and communication, yet they should function strictly as augmentative tools rather than independent decision-makers. To our knowledge, this is the first study to systematically benchmark LLM reasoning against an official international safeguarding framework in sport. Future research should evaluate interactive and multilingual applications to enhance contextual accuracy and equitable safeguarding outcomes.