What is the risk of recurrent concussion in children and adolescents aged 5–18 years? A systematic review and meta-analysis
Bibliographic record
Abstract
Objective We aimed to examine the risk of concussion in children with a previous history of concussion. Design Systematic review and meta-analysis. The primary outcome was number of children with and without a previous lifetime history of concussion who sustained a diagnosed concussion within each study period. Risk of bias was assessed using the Newcastle-Ottawa Scale. A random effects model was used to estimate a pooled risk ratio (RR) with corresponding 95% CIs; results were summarised in forest plots. Data sources Four electronic databases (MEDLINE, Embase, CINAHL, SPORTDiscus) and selected reference lists were searched (PROSPERO registration No CRD42019135462). Eligibility criteria Original English language peer-reviewed publications that compared concussion risk in children aged 5–18 years with and without a previous concussion history in which risk estimates were reported or able to be calculated. Results Of 732 identified studies, 7 studies representing 23 411 children (risk of bias range, 7–9; maximum possible score=9) were included for meta-analysis. Pooled risk of sustaining a concussion was more than three times greater in children with a previous concussion compared with those with no previous concussion (RR=3.64; 95% CI: 2.68 to 4.96; p<0.0001; I 2 =90.55%). Unreported sex-stratified data precluded direct comparison of concussion risk in male versus female athletes. Conclusion Previously concussed children have four times the risk of sustaining a concussion compared with those with no previous concussion history. This should be a consideration for clinicians in return to sport decision-making. Future studies examining subsequent recurrent concussion in youth sports must consider sex differences.
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".