A Concussion Management Policy Change Promoted Earlier Initiation of Rehabilitation Services and Improved Clinical Recovery Outcomes in Concussion
Bibliographic record
Abstract
CONTEXT: In line with emerging research, an interprofessional specialty concussion clinic instituted a policy change permitting earlier physiotherapy-based treatment entry. Our objective was to determine the effect of this policy change on concussion recovery outcomes. DESIGN: Secondary analysis of prospectively collected clinical data. METHODS: 600 youth with concussion were included. Active rehabilitation was initiated ≥4 weeks (prepolicy) or ≥2 weeks (postpolicy) postconcussion based on institutional policy. Cox proportional hazard models, linear mixed models, and chi-square analyses were conducted. RESULTS: The postpolicy group (median = 22 d [interquartile range: 17-27]) started treatment earlier than the prepolicy group (median = 26 d [interquartile range: 24-30], P < .001). Length of episode of care (χ2(1) = 11.55, P < .001, odds ratios = 1.49; 95% confidence interval, 1.19-1.88); rehabilitation (χ2(1) = 9.47, P = .002, odds ratios = 1.73, 95% confidence interval, 1.22-2.45]); and total recovery (χ2(1) = 11.53, P < .001, odds ratios = 1.49; 95% confidence interval, 1.18-1.88) were reduced in patients postpolicy change. A significant interaction effect was found for total postinjury symptom (F2,320 = 3.59, P = .03) and symptom change scores (F2,315 = 5.17, P = .006), with the postpolicy group having faster symptom resolution over time. No group differences were observed for persisting symptoms. CONCLUSIONS: Earlier rehabilitation initiation occurred as intended following an institutional policy change, which had small, but significant, effects on recovery outcomes in youth with concussion. Health care providers should adopt policies to encourage early active rehabilitation services after concussion.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".