Non-COVID-19 cardiovascular pathology from return-to-play screening in college athletes after COVID-19
Bibliographic record
Abstract
OBJECTIVE: Concerns for cardiac involvement after SARS-CoV-2 infection led to widespread cardiac testing in athletes. We examined incidental non-COVID-19 cardiovascular pathology in college athletes undergoing postinfection return-to-play screening. METHODS: The Outcomes Registry for Cardiac Conditions in Athletes was a nationwide prospective multicentre observational cohort study that captured testing and outcomes data from 45 institutions (September 2020-June 2021). Athletes with an ECG and transthoracic echocardiogram (TTE) and no pre-existing conditions were included. Findings were defined as major (associated with sudden cardiac death or requiring intervention), minor (warrants surveillance), incidental (no follow-up needed) or uncertain significance (abnormal with subsequent normal testing). RESULTS: Athletes with both ECG and TTE (n=2900, mean age 20±1, 32% female, 27% black) were included. 35 (1.2%) had ECG abnormalities. Of these, 2 (5.7%) had TTE abnormalities indicating cardiomyopathy (hypertrophic-1, dilated-1), and 1 with normal TTE had atrial fibrillation. Of 2865 (98.8%) athletes with a normal ECG, 54 (1.9%) had TTE abnormalities: 3 (5.6%) with aortic root dilatation ≥40 mm, 15 (27.8%) with minor abnormalities, 25 (46.3%) with incidental findings and 11 (20.4%) with findings of uncertain significance. Overall, 6 (0.2%) athletes had major conditions; however, coronary anatomy and aortic dimensions were inconsistently reported and pathology may have been missed. CONCLUSION: Major non-COVID-19 cardiovascular pathology was identified in 1/500 college athletes undergoing return-to-play screening. In athletes without ECG abnormalities, TTE's added value was limited to pathological aortic root dilatation in 1/1000 athletes and minor abnormalities warranting surveillance in 1/160 athletes. Two-thirds of findings were incidental or of uncertain significance.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".