Mining Minds: Implementing a Concussion Registry in a Tertiary Care Clinic
Bibliographic record
Abstract
Abstract Background:Hundreds of thousands of Canadians experience a concussion each year. The Public Health Agency of Canada estimates the associated costs at over $150 million a year. This paper examines how a registry for concussions serves as a proactive measure to reduce the burden of concussion research and improve patient flow through an outpatient clinic.Methods:The Traumatic Brain Injury Registry Database (TBIRD) collects data at the initial clinic visit and enters consenting patients’ information into a secure database. The data collected are internationally accepted Common Data Elements (CDEs). Enabling systematic standardized characterizations of the concussion injury history, social determinants of health and current symptoms. Results:Over 350 participants to date, 58.5% are female, 41.4% male. The most common mechanism of injury is transportation-related at 57.1%. The mean age of participants was 44 (SD15).Conclusions:This registry is an effective method of recruiting participants for future research within the hospital and broader studies. It provides researchers with a set of patients willing to help further understand concussions and reduces patient burden caused by multiple research teams approaching individuals. The lessons learned are applicable to other clinics to help develop a standardized and patient-friendly approach to concussion research to improve care.
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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 | 0.029 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".