Activity and Recovery Among Youth With Concussion: A Meta-analysis
Bibliographic record
Abstract
CONTEXT: Concussions in youth are highly prevalent. Previously, rest was prescribed to prevent adverse outcomes; however, early return to activity is emerging to improve the recovery trajectory. OBJECTIVES: To evaluate the effectiveness of early return to physical and social activity interventions on recovery outcomes in youth with concussion. DATA SOURCES: A systematic review was conducted up to October 2022. STUDY SELECTION: We included randomized controlled trials (RCTs) and non-RCTs that reported effects of activity-based interventions on symptoms, quality-of-life (QoL), and return to preinjury activity levels in children and youth after a concussion. DATA EXTRACTION: Three authors independently extracted data on publication year and country, study setting and design, sample size, participant demographics, intervention, outcome(s), and author conclusion. Meta-analysis was conducted on appropriate RCTs. RESULTS: Twenty-four studies were included in the final review, of which 10 were RCTs. There was a significant effect of activity interventions on symptom reporting (standardized mean difference, 0.39 [95% confidence interval, 0.15 to 0.63]; I2, 0%; P = .002). There was not a significant effect of activity-based interventions on QoL (mean difference, -0.91 [95% confidence interval, -7.76 to 5.94]; I2, 0%; P = .79). No meta-analysis was performed on return to preinjury activity levels because of insufficient number of RCTs conducted. LIMITATIONS: One outcome was excluded from the meta-analysis. Interventions emphasizing social activity were lacking. CONCLUSIONS: Findings indicate that activity-based interventions may significantly improve concussion symptoms. There is insufficient data to understand the effect of activity-based intervention on QoL and return to preinjury activity levels.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.046 |
| Bibliometrics | 0.005 | 0.005 |
| 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".