When is it time to start rehab? exploring the optimal timing to initiate active rehabilitation for concussion management in children and adolescents
Bibliographic record
Abstract
Objective Estimate the influence of time to initiation of active rehabilitation on post-concussion symptom (PCS) severity in youth who are slow to recover from concussion. Design Retrospective analysis of a prospective cohort Setting Concussion clinic of a tertiary care Paediatric Trauma Centre in Canada. Participants 569 youth (14.3±2.3 years) with persistent PCS. Clinic patients’ information is entered prospectively in a clinical database and participants were selected for this study if they 1) participated in the active rehabilitation program, and 2) had available PCS assessments at the intake and follow-up visits. Intervention Active rehabilitation consisting of: aerobic exercise, coordination exercises and, education/motivation. The intervention was initiated with a Physical Therapist in the Concussion clinic continued as a daily home program. The independent variable was time to initiation of the active rehabilitation program measured in weeks (2, 3, 4, 5, 6+). Outcome measure Symptom severity measured by the PCS scale of the SCAT3 at follow-up visit, 2 weeks after initiation of intervention.Main results: Patients initiating active rehabilitation 2 weeks post-injury were significantly less symptomatic at follow-up (Mdn PCS score=5) compared to those starting five (Mdn=17) and six weeks or more (Mdn=17.5) (p=0.0002). Those starting at 3 (Mdn=9) and 4 (Mdn=10) weeks also had significantly less severe symptoms compared to those starting 6 weeks or later (p<0.05). Conclusions The findings suggest that children benefit more from active rehabilitation if it is initiated between two and four weeks post-injury, and have poorer outcomes if it is delayed beyond 6 weeks post-injury. Competing interests None.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".