Cardiovascular complications and outcomes among athletes with COVID-19 disease: a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Current evidence still emerging regarding the risk of cardiovascular (CV) sequel associated with coronavirus disease 2019 (COVID-19) infection, and considerable replicated studies are needed to ensure safe return-to-play. Therefore, we aimed in this systematic review to measure the prevalence of CV complications suffered by COVID-19 athletic patients, explore the outcomes, optimal approaches to diagnoses, and safe return-to-play considerations. METHODS: A systematic search on post COVID-19 infection quantitative studies among athletes was conducted following MeSH terms in Medline, Cochrane Library, Ovid, Embase and Scopus (through 15 January 2022). We included peer-reviewed studies reported athletes' CV complications and the outcomes post COVID-19 infection. Editorials, letters, commentaries, and clinical guidelines, as well as duplicate studies were excluded. Studies involving non-athletic patients were also excluded. Quality assessment was performed using Newcastle-Ottawa Scale. RESULTS: We included 15 eligible articles with a total of 6229 athletes, of whom 1023 were elite or professional athletes. The prevalence of myocarditis ranged between 0.4% and 15.4%, pericarditis 0.06% and 2.2%, and pericardial effusion between 0.27% and 58%. Five studies reported elevated troponin levels (0.9-6.9%). CONCLUSIONS: This study provides a low prevalence of CV complications secondary to COVID-19 infection in short-term follow-up. Early recognition and continuous assessment of cardiac abnormality in competitive athletes are imperative to prevent cardiac complications. Establishing a stepwise evaluation approach is critical with an emphasis on imaging techniques for proper diagnosis and risk assessment for a safe return to play.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it