A population-based study of paediatric emergency department and office visits for concussions from 2003 to 2010
Bibliographic record
Abstract
BACKGROUND: There is a paucity of information regarding descriptive epidemiology of paediatric concussions over time, and few studies include both emergency department (ED) and physician office visits. OBJECTIVE: To describe trends in visits for paediatric concussions in both EDs and physician offices according to age and sex. A secondary objective was to describe the cause of concussion for children treated in EDs. METHODS: A retrospective, population-based study using linked health administrative data from all concussion-related visits to the ED or a physician office by school-age children and youth (three to 18 years of age) in Ontario between April 1, 2003 and March 3, 2011 was conducted. RESULTS: The number of children evaluated in both EDs and a physician offices increased between 2003 and 2010, and this linear trend was statistically significant (P=0.002 for ED visits and P=0.001 for office visits). The rate per 100,000 increased from 466.7 to 754.3 for boys and from 208.6 to 440.7 for girls during the study period. Falls accounted for approximately one-third of the paediatric concussions. Hockey/skating was the most common specific cause of paediatric sports-related concussions. CONCLUSIONS: The increasing use of health care services for concussions is likely related to changes in incidence over time and increased awareness of concussion as a health issue. Evidence-based prevention initiatives to help reduce the incidence of concussion are warranted, particularly in sports and recreation programs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".