Concussion and mild traumatic brain injury (mtbi): towards a better integration of the two constructs
Bibliographic record
Abstract
Objective To analyse the potential ways to reunite the mTBI and concussion constructs in order to harmonise screening, diagnosis and management. Design Narrative review. Setting In Quebec, the initial management of concussions sustained in a sport or school-based setting falls under the authority of the Ministry of education and sports whereas the Ministry of health governs the clinical management of mTBI. The recent work of a task force on concussions in Quebec has emphasised the need to achieve a unified understanding and operationalization of the two constructs so that persons affected receive consistent information and care. Main results Sport and school-based concussion management is generally based on the Concussion In Sport Group definition whereas the Quebec health care system relies on the 5 diagnostic criteria proposed by the WHO task force in 2004. Although the 5 classification criteria of mTBI include normal values, the actual definition requires the presence of one or more abnormal criteria. In a public health care system, such a definition structures access to limited public resources. However unfavourable evolution can occur in the absence of phenomena such as LOC or PTA. Yet these cases represent a potentially serious health condition if proper identification and management is not initiated. Conclusion So far, sport, education and health care stakeholders in Quebec have agreed that the constructs of concussion and TBI must be revisited and better integrated. This process raises challenges and opportunities in terms of operationalization and management. Studies on prognostic indicators should guide this analysis. Conclusion Competing interests Pierre Frémont is chair of the Canadian Concussion Collaborative. 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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".