Clinical recovery from concussion–return to school and sport: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective To define the time frames, measures used and modifying factors influencing recovery, return to school/learn (RTL) and return to sport (RTS) after sport-related concussion (SRC). Design Systematic review and meta-analysis. Data sources 8 databases searched through 22 March 2022. Eligibility criteria Studies with diagnosed/suspected SRC and interventions facilitating RTL/RTS or investigating the time and modifying factors for clinical recovery. Outcomes included days until symptom free, days until RTL and days until RTS. We documented study design, population, methodology and results. Risk of bias was evaluated using a modified Scottish Intercollegiate Guidelines Network tool. Results 278 studies were included (80.6% cohort studies and 92.8% from North America). 7.9% were considered high-quality studies, while 23.0% were considered high risk of bias and inadmissible. The mean days until symptom free was 14.0 days (95% CI: 12.7, 15.4; I 2 =98.0%). The mean days until RTL was 8.3 (95% CI: 5.6, 11.1; I 2 =99.3%), with 93% of athletes having a full RTL by 10 days without new academic support. The mean days until RTS was 19.8 days (95% CI: 18.8, 20.7; I 2 =99.3%), with high heterogeneity between studies. Several measures define and track recovery, with initial symptom burden remaining the strongest predictor of longer days until RTS. Continuing to play and delayed access to healthcare providers were associated with longer recovery. Premorbid and postmorbid factors (eg, depression/anxiety, migraine history) may modify recovery time frames. Though point estimates suggest that female sex or younger age cohorts take longer to recover, the heterogeneity of study designs, outcomes and overlap in CIs with male sex or older age cohorts suggests that all have similar recovery patterns. Conclusion Most athletes have full RTL by 10 days but take twice as long for an RTS. PROSPERO registration number CRD42020159928.
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| 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) | 0.004 | 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".