Results From the Big Ten COVID-19 Cardiac Registry: Impact of SARS-COV-2 on Myocardial Involvement
Bibliographic record
Abstract
OBJECTIVE: COVID-19 has been associated with myocardial involvement in collegiate athletes. The first report from the Big Ten COVID-19 Cardiac Registry (Registry) was an ecological study that reported myocarditis in 37 of 1597 athletes (2.3%) based on local clinical diagnosis. Our objective was to assess the relationship between athlete and clinical characteristics and myocardial involvement. DESIGN: Cross-sectional study. SETTING: We analyzed data from 1218 COVID-19 positive Big Ten collegiate athletes who provided informed consent to participate in the Registry. PARTICIPANTS: 1218 athletes with a COVID-19-positive PCR test before June 1, 2021. ASSESSMENT OF INDEPENDENT VARIABLES: Demographic and clinical characteristics of athletes were obtained from the medical record. MAIN OUTCOME MEASURES: Myocardial involvement was diagnosed based on local clinical, cardiac magnetic resonance (CMR), electrocardiography, troponin assay, and echocardiography. We assessed the association of clinical factors with myocardial involvement using logistic regression and estimated the area under the receiver operating characteristic (ROC) curve. RESULTS: 25 of 1218 (2.0%) athletes met criteria for myocardial involvement. The logistic regression model used to predict myocardial involvement contained indicator variables for chest pain, new exercise intolerance, abnormal echocardiogram (echo), and abnormal troponin. The area under the ROC curve for these indicators was 0.714. The presence of any of these 4 factors in a collegiate athlete who tested positive for COVID-19 would capture 55.6% of cases. Among noncases without missing data, 86.9% would not be flagged for possible myocardial involvement. CONCLUSION: Myocardial involvement was infrequent. We predicted case status with good specificity but deficient sensitivity. A diagnostic approach for myocardial involvement based exclusively on symptoms would be less sensitive than one based on symptoms, echo, and troponin level evaluations. Abnormality of any of these evaluations would be an indication for CMR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".