P.087 “Reported Brain Injury” Time trends within two Canadian health surveys over two decades
Bibliographic record
Abstract
Background: An “epidemic” of concussions has been widely reported. We explored the annual incidence of reported concussion or other brain injury, over 20 years within Canada in order to explore the magnitude of this reported epidemic. Methods: Two Canadian nationally representative health surveys have serially collected injury data associated with disability. The National Population Health Survey (NPHS) (1994-9) collected data on “concussion”, and the Canadian Community Health Survey (CCHS) (2000-current) has collected data on “concussion or other brain injury”. Data on respondents 12 years and older reporting concussion with or without other brain injury within the past year were examined in order to produce serial incidence data. Results: Nationally representative data were available biennially from 1994/95 through 2013/14 with the exception of 2007/08 and 2011/12. The incidence of reported concussions, or concussions and other brain injury has been stable until 2005/06 when the reported annual incidence started an upward slope to levels 250% higher (p<0.001) without any apparent stabilization by 2013/14, when approximately 1 in 200 Canadians 12 years and older report concussion or other brain injury as their most significant injury associated with disability in the previous 12 months. Conclusions: There is currently a pandemic of reported brain injury in Canada.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".