Safe return to play after protocol-based concussion management by a team therapist: a prospective study
Bibliographic record
Abstract
Objective To analyse the safety of a multidisciplinary concussion management protocol where a team therapist is involved in return-to-play (RTP) decisions. Design Prospective, pre- post-intervention cohort study over 4 seasons of competition Setting School-based American football program (4 teams; grades 8–12) Participants: 672 players x year (11–17 year old) for a total of 27741 athlete-exposure. Intervention A protocol based on the Concussion In Sport Group recommendations was implemented. A computerised neuropsychological (CNP) test was used at baseline and prior to clearance for unrestricted training. For seasons 1–2, the protocol required the team physician to make all RTP decision while defined criteria allowed the team physiotherapist to make the vast majority of the RTP decisions over seasons 3–4. Outcome measures The primary outcome was the early recurrence (ER) of concussion symptoms following RTP (defined as during the same season). Results A total of 119 concussions were identified (55 and 64 for the first and last 2 seasons, respectively; incidence rate 4, 3: 1000 athlete-exposures). At the time of the first clearance decision, the CNP test contributed to a negative RTP decision in 67% of cases. During seasons 1–2 no ER was observed. During season 3 one injury unrelated to football resulted in one case of ER prior to clearance and, during season 4, one case of ER occurred during the second game, 11 days after clearance. Conclusions Safe management of concussions was achieved whether the team physician or the team therapist was responsible for the application of the terms of the protocol. Competing interests Pierre Frémont chair of the Canadian Concussion Collaborative. Édith Castonguay is the physiotherapist for the football program described in this study. Francesco Pepe-Esposito is Head coach of the football program described in this study. 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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 0.001 |
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".