Increasing Incidence of Concussion: True Epidemic or Better Recognition?
Bibliographic record
Abstract
OBJECTIVES: To provide updated estimates of the incidence of concussion from all causes diagnosed by all physicians in a large jurisdiction, as previous studies have examined only single causes of injury or from smaller specific populations. DESIGN: Physician Billing and National Ambulatory Care Reporting System (NACRS) databases were used to identify all Ontario residents with a diagnosis of concussion (ICD-9 850.0 and ICD-10 S06.0) made by physicians between 2008 and 2016, excluding those with moderate to severe traumatic brain injury. RESULTS: In total, 1 330 336 people were diagnosed with a concussion between 2008 and 2016. The annual average was 147 815, and 79% were diagnosed in the emergency department. The average annual incidence was 1153 per 100 000 residents. Incidence varied by age, sex, and geography; children younger than 5 years had the highest incidence of concussion, more than 3600 per 100 000 individuals of that age group. Males had higher incidence than females except in older than 65 years age groups. There was a Pearson correlation (+0.669) between sustaining a concussion and living in rural locations. CONCLUSION: The annual incidence of approximately 1.2% of the population is the highest rate of concussion ever reported thorough sampling methods and may represent a closer estimate of the true picture of concussion. Findings may inform future concussion treatment and healthcare planning.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".