Erica Brugin

Background: Pre-participation screening (PPS) is widely used to evaluate athletes before sport participation, traditionally focusing on cardiovascular risk prevention. However, musculoskeletal assessment performed during PPS may also allow early identification of biomechanical and structural abnormalities that can affect sport participation during growth. Objectives: This study aimed to describe the prevalence, clinical characteristics, and management pathways of musculoskeletal alterations detected during PPS in young athletes, with particular focus on referral patterns to pediatric Physical Medicine and Rehabilitation (PM&R) specialists. Methods: This retrospective observational study included athletes aged ≤15 years who underwent PPS between January 2022 and December 2023 at the Sports Medicine Unit of Noale (ULSS 3 Serenissima, Venice, Italy). Musculoskeletal screening was systematically performed during PPS. Athletes presenting newly detected abnormalities were either managed conservatively by the sports physician or referred for PM&R evaluation. Clinical indications for referral, imaging investigations, diagnoses, and treatment strategies were analyzed. Results: A total of 707 athletes underwent PPS during the study period. After applying exclusion criteria, 635 athletes were eligible for analysis. Musculoskeletal alterations were identified in 141 athletes (22.2%). Among them, 54 athletes were referred for PM&R evaluation and 40 completed the consultation. No statistically significant differences were observed between referred and non-referred athletes. The most frequent reasons for referral were suspected scoliosis (n = 22), foot alignment disorders n = 18), thoracic hyperkyphosis (n = 5), and lower limb length discrepancy (n = 4). Specialist evaluation resulted in conservative management in most cases, including corrective exercise programs and orthotic interventions, while one athlete required surgical treatment for congenital scoliosis. Conclusions: Musculoskeletal screening during PPS identified a broad spectrum of findings in young athletes, including clinically relevant abnormalities requiring specialist evaluation and management. These findings suggest that musculoskeletal assessment may provide an opportunity for early clinical identification within PPS; however, prospective studies are needed to evaluate diagnostic accuracy, long-term clinical benefit, injury prevention, and cost-effectiveness before routine implementation can be recommended.

Background: Artificial intelligence (AI) has shown promise in the interpretation of electrocardiograms (ECGs) using signal-based deep learning models. In parallel, large language models (LLMs) have gained increasing visibility in clinical practice, including exploratory applications in ECG analysis. Whether a general-purpose LLM can meaningfully discriminate cardiovascular disease from athlete ECGs during PPS remains unknown. We aimed to evaluate the diagnostic performance of a general-purpose LLM for this task. Methods: In this multicentre diagnostic accuracy study, we evaluated a commercially available LLM (ChatGPT, version 5) in 2950 competitive athletes undergoing PPS. All athletes underwent resting 12-lead ECG, with second- and third-line investigations performed when clinically indicated. The reference outcome was confirmed cardiovascular disease after full diagnostic work-up (n = 450, 15.3%). For each ECG, the LLM generated a numeric score (0–100) representing the inferred likelihood of underlying disease using a standardized prompt and without task-specific fine-tuning. Discriminative performance was assessed using receiver operating characteristic (ROC) analysis. Misclassification patterns were analysed according to International ECG Criteria. Results: GPT-derived scores demonstrated a marked floor effect, with a median value of 0 (IQR 0–2) in both diseased and non-diseased athletes and substantial overlap between groups. The area under the ROC curve was 0.52 (95% CI 0.49–0.55), indicating performance close to random classification. At the Youden-derived threshold, 79% of athletes with confirmed disease were incorrectly classified as negative. False-negative cases were predominantly characterized by borderline ECG patterns (82%), and a substantial number of red-flag ECG abnormalities were also missed. Conclusions: In this PPS cohort, a general-purpose LLM used in a naïve configuration showed no clinically meaningful ability to discriminate between cardiovascular disease and athlete ECGs. Without task-specific training or domain adaptation, such models should not be used for diagnostic triage in athlete screening.

Recently, electronic sports (eSports) became one of the growing forms of new media due to the wide diffusion of games and online technologies. Even if there is still a debate about the definition and characterization of eSports, eAthletes train heavily, compete in tournaments, must abide by competition, association, and governing body rules, just like all other athletes. Furthermore, as in any other competitive discipline, there can be injuries. Aberrant sitting posture, repetitive movements, screen vision, prolonged playing hours, and a sedentary lifestyle can lead to several medical hazards in musculoskeletal, ophthalmology, neurological, and metabolic systems. Moreover, several cardiovascular changes occur in eAthletes. This paper aims to explore the different injuries that can occur in a professional eAthlete, suggesting how every high-level gamer could benefit from a pre-participation evaluation and a correct injury prevention strategy.

Background: Child musculoskeletal (MSK) diseases are common and, even if often benign, sometimes can lead to significant impairment in the future health of children. Italian pre-participation evaluation (PPE), performed by a sports medicine physician, allows for the screening of a wide range of children every year. Therefore, this study aims to evaluate the feasibility and the acceptability of pGALS (pediatric Gait, Arms, Legs and Spine) screening, a simple pediatric MSK screening examination, when performed as part of a routine PPE. Methods: Consecutive school-aged children attending a sports medicine screening program were assessed with the addition of pGALS to the routine clinical examination. Practicability (time taken) and patient acceptability (discomfort caused) were recorded. Results: 654 children (326 male, mean age 8.9 years) were evaluated through pGALS. The average time taken was 4.26 min (range 1.9–7.3 min). Acceptability of pGALS was deemed high: time taken was “adequate” (97% of parents) and caused little or no discomfort (94% of children). Abnormal MSK findings were common. Conclusions: pGALS is a practical and acceptable tool to perform in sports medicine PPE, even if performed by a non-expert in MSK medicine. Although common, abnormal MSK findings need to be interpreted in the global clinical context and assessment.