Return to play of young and adult professional athletes after COVID-19: A scoping review
Bibliographic record
Abstract
Background/objective: Given the persistence of COVID-19 under various facets and mutations, there is an urgent need to understand the debate on a safe return to play for professional athletes (young and adults) recovering from the infection. This work offers a scoping and comprehensive review on the topic during the first two years of the pandemic event by providing an identification of main clusters of research, relevant gaps and significant insights for future investigation. Methods: , SCIENCEDIRECT, and SCOPUS. Further criteria for selection are: Time range of 2020-2022; Scope: Return to play of professional athletes recovering from COVID-19 infection; 3) Types of publications: Research papers, reviews, practice guidelines, case reports; 4) Language: English. Two independent researchers performed a quality check on a random sample (n = 30%) of publications. Results: Main results reveal four research clusters deepening the analysis on: myocarditis, cardiac diseases and return to play, training and rehabilitation, mass screening and risk assessment, and sport and bio-psycho-social sphere for a safe return to play. Major collaborations occur between UK-South Africa, UK-USA, USA-Canada, and USA-Australia. Conclusions: Important gaps refer to a lack of investigation on a safe return to play for female athletes in mostly all sports disciplines; on the other hand, sport and the bio-psycho-social sphere of the athlete is a fast-growing topic. Both deserve further attention in the immediate future to improve ad-hoc sport and exercise practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".