8.10 Monitoring sport-related concussion in high performance sport: results of an interdisciplinary clinical model of practice
Bibliographic record
Abstract
Objective The objective of this practical case study was to share knowledge and results on the efficacy of an interdisciplinary clinical model for the monitoring of SRC and return to performance. Design A systematic SRC monitoring approach was conducted for the past 4 years. Setting Treatments occurred at the medical clinic of a Canadian national sport institute (Montréal, Québec). Participants A total of 127 elite athletes (nF = 72; nM = 55) from 26 different sports were treated for SRC. Interventions (or Assessment of Risk Factors) Systematic and holistic interventions included a SCAT-5, autonomic system, cervical, cognitive, vestibular and visual evaluations. Outcome Measures Occurrence of SRCs, recovery time, number of treatments were recorded and analyzed. Main Results A total of 157 SRCs (57% F, 43% M) were diagnosed and were mostly occurring in short track speed skating (29%), judo (20%), figure skating (9%) and water-polo (9%). SRCs mainly occurred during training (53%) and competition (28%). SRCs were resolved between 15 to 28 (37%), more than 28 (29%), between 8 to 14 (25%), and in 7 (4%) days. Athletes received 1 to 5 (80%), 6 to 11 (15%), or more than 12 (5%) treatments. Conclusions These results helped to better understand the contributions of an interdisciplinary clinical model of practice for the return to performance.
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 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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".