The risk of all-cause injury and site-specific injury in athletes after concussion: a systematic review and meta-analysis
Bibliographic record
Abstract
This systematic review and meta-analysis aimed to quantify the risk of subsequent all-cause, recurrent concussion, upper extremity, and lower extremity injuries in athletes with a history of sport-related concussion. Following PRISMA guidelines, cohort studies published from inception through August 2025 were retrieved from PubMed, Cochrane Library, Embase, Web of Science, and EBSCO. The methodological quality of included studies was evaluated using the Newcastle-Ottawa Scale (NOS). Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using random-effects models. Pre-specified subgroup and meta-regression analyses were conducted to investigate sources of heterogeneity, including injury timing, study design, sport type, and athlete competition level. Nineteen cohort studies involving 86,879 athletes were included. Athletes with a history of concussion had significantly higher odds of sustaining a subsequent all-cause injury (OR = 1.93; 95% CI = 1.39–2.68). The risk was most pronounced for recurrent concussion (OR = 3.06; 95% CI = 1.81–5.17), and was also significantly elevated for upper extremity (OR = 1.76; 95% CI = 1.10–2.81) and lower extremity injuries (OR = 1.49; 95% CI = 1.06–2.09). Given the high heterogeneity observed in the primary outcomes (I 2 > 90%), the pooled effect sizes should be interpreted with caution as average associations across varying study contexts, rather than as precise predictions applicable to all settings. Subgroup analysis revealed that injury risk was statistically significant in studies with follow-up periods beyond six months (OR = 1.94) but not for shorter durations. The association was strongest and statistically significant among college athletes (OR = 2.29; 95% CI = 1.53–3.44), while estimates for professional and high school athletes were not significant. Meta-regression identified sport type as a significant moderator of injury risk ( p = 0.038). A history of concussion significantly increases the risk of subsequent injuries, with the odds being highest for recurrent concussion. The persistence of this risk beyond six months suggests that clinical recovery does not equate to full functional recovery. These findings underscore the need for enhanced return-to-play protocols that incorporate objective functional assessments and targeted rehabilitation to mitigate the heightened vulnerability to injury in post-concussed athletes.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.049 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".