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Record W4221109453 · doi:10.46747/cfp.6803e100

Assessment of a collaborative concussion management strategy in a school-based sport program

2022· article· en· W4221109453 on OpenAlexaffvenueabout
Pierre Frémont, Francesco Pepe Esposito, Edith Castonguay, James D. Carson

Bibliographic record

VenueCanadian Family Physician · 2022
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of TorontoUniversité Laval
Fundersnot available
KeywordsConcussionAthletesMedicineFootballProtocol (science)Physical therapyIncidence (geometry)Football teamCohortInjury preventionPoison controlEmergency medicineAlternative medicineInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To analyze the implementation of a concussion management protocol in which a team physiotherapist is involved in the identification of concussions and return-to-play (RTP) decisions. DESIGN: A prospective injury surveillance cohort study in a school-based Canadian football program (4 teams; grades 8 to 12) over 4 years. For years 1 to 2, the team physician made all RTP decisions; over years 3 to 4, the team physiotherapist was allowed to make some RTP decisions using pre-established criteria defined in the protocol. SETTING: A high school in Québec, Que. PARTICIPANTS: Male student athletes between 11 and 17 years old. MAIN OUTCOME MEASURES: Same-season recurrence (SSR) of concussion symptoms following RTP. RESULTS: A total of 119 concussions were identified (55 during the first 2 years and 64 during the last 2 years) during 27,741 athlete-exposures in 672 athlete-years for an incidence rate of 4.3 per 1000 athlete-exposures. During years 1 to 3, no SSR was observed following RTP clearance. During year 4 there was 1 case of SSR that occurred 11 days after clearance. The overall SSR rate of concussion symptoms following RTP clearance was 0.8%. CONCLUSION: A very low rate of SSR was achieved whether the team physician made all RTP decisions or the team physiotherapist was allowed to make some of the RTP decisions through the terms of the protocol.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score0.891

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.348
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2022
Admission routes3
Has abstractyes

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