Determining the accuracy of the Canadian Hospitals Injury Reporting and Prevention Program for the representation of the rates of mild traumatic brain injuries in Quebec
Bibliographic record
Abstract
INTRODUCTION: The recent rise in mild traumatic brain injuries (mTBI) in the pediatric population has been documented by many studies in Canada and the United States. The objective of our study was to compare mTBI rates from the Canadian Hospital Injury Reporting and Prevention Program (CHIRPP) in Montréal with population-based rates (Quebec mTBI rates). METHODS: We calculated CHIRPP's mTBI rates via two methods: (1) using all CHIRPP injuries as the denominator; and (2) using the number of children aged 0 to 17 years living within 5 km of either of two CHIRPP centres in Montréal as the denominator. We plotted CHIRPP's mTBI rates against the provincial rates and compared them according to sex and age. RESULTS: Whether using all CHIRPP injuries or the number of children aged 0 to 17 years living within 5 km of either CHIRPP centre in Montreal as the denominator, CHIRPP paralleled the fluctuations seen in Quebec's rates between 2003 and 2016. When stratifying by sex and age, CHIRPP was better at estimating the population-based rates for the youngest (0 to 4 years) and the oldest (13 to 17 years) age groups. CONCLUSION: CHIRPP in Montréal proved a valid tool for estimating the variations in rates of mTBI in the population. This suggests that CHIRPP could also be used to estimate population-based rates of other types of injuries.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".