Bibliographic record
Abstract
Background Paediatric concussions are an important reason for children to seek care from their physician or in the Emergency Department (ED). Although studies are suggesting that paediatric concussions are increasing for sports-related diagnoses, there is a paucity of population-based information for all causes and including both office and emergency department visits. The objective of this study was to provide population-based estimates of paediatric concussions by age, sex, and external cause of injury. Methods The National Ambulatory Care Reporting System (NACRS) was used to report on children in Ontario, Canada who attended an ED with a discharge diagnosis of concussion (S06) from 2003 to 2006. Children whose physician billed an office visit for a concussion based on the Ontario Health Insurance Program were also included. Results There were over 80 000 children treated for concussions in Ontario over the seven study years. Fifty-two per cent were treated in physician's offices. Boys incurred 66% of all concussions, and the incidence was highest among 14-year-olds to 16-year-olds. The incidence of concussion is increasing, with an increase of 45% in physicians' offices, and an increase of 26% in EDs. Within the ED, 34% of all concussions were due to falls, and 18% were due to hockey or skating. Conclusion Concussions are an important cause of morbidity within the paediatric population in Ontario, and the incidence is increasing. It is important to capture both physician visits and ED visits when calculating incidence. Evidence-based strategies can be used to reduce the burden, with particular emphasis on falls and sports-related concussions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".