Higher rates of concussion following COVID-19 infection in high school athletes
Bibliographic record
Abstract
OBJECTIVE: To compare concussion rates (CRs) over one academic year in high school athletes with and without a COVID-19 infection prior to concussion. METHODS: Illness and concussion were prospectively reported for male and female high school athletes across six states over one academic year in the Players Health Rehab surveillance system. Concussion was truncated to 60 days following recovery and return to sport from COVID-19. CRs were estimated per 1000 athletes per academic year and stratified by those who tested positive for COVID-19 infection (with COVID-19) and those who did not (no COVID-19). Poisson regression analyses estimated rate ratio (RR) of concussion controlling for state, gender and an offset of the log athlete participation (with COVID-19 and no COVID-19). RESULTS: Of 72 522 athletes, 430 COVID-19 infections and 1273 concussions were reported. The CR was greater in athletes who reported COVID-19 (CR=74.4/1000 athletes/year, 95% CI 49.6 to 99.3) compared with those who did not (CR=17.2, 95% CI 16.3 to 18.2). Athletes with recent COVID-19 had a threefold higher rate of concussion (RR=3.1, 95% CI 2.0 to 4.7). CONCLUSION: Athletes returning from COVID-19 had higher CRs than those who did not experience COVID-19. This may be related to ongoing COVID-19 sequelae or deconditioning related to reduced training and competition load during the illness and when returning to sport. Further research is needed to understand the association of recent COVID-19 infection and concussion in order to inform preventive strategies.
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 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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".