Common Data Elements for Concussion in Tertiary Care: Phase One in Ontario
Bibliographic record
Abstract
BACKGROUND: Standardized data collection for traumatic brain injury (TBI) (including concussion) using common data elements (CDEs) has strengthened clinical care and research capacity in the United States and Europe. Currently, Ontario healthcare providers do not collect uniform data on adult patients diagnosed with concussion. OBJECTIVE: The Ontario Concussion Care Strategy (OCCS) is a collaborative network of multidisciplinary healthcare providers, brain injury advocacy groups, patient representatives, and researchers with a shared vision to improve concussion care across the province, starting with the collection of standardized data. METHODS: The International Framework of Functioning Disability and Health was selected as the conceptual framework to inform the selection of CDEs. The CDEs recommended by the OCCS were identified using key literature, including the National Institute of Neurological Disorders and Stroke-Zurich Consensus Statements for concussion in sport and the Ontario Neurotrauma Foundation Concussion/mTBI clinical guidelines. RESULTS: The OCCS has recommended and piloted CDEs for Ontario that are readily available at no cost, clinically relevant, patient friendly, easy to interpret, and recognized by the international scientific community. CONCLUSIONS: The implementation of CDEs can help to shift Ontario toward internationally recognized standard data collection, and in so doing yield a more comprehensive evidence-based approach to care while also supporting rigorous research.
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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 | 0.039 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".