Bibliographic record
Abstract
To investigate the epidemiology of concussion in Canadian children and youth. We analyzed data from the self-administered questionnaire microdata files of the National Longitudinal Survey of Children and Youth (NLSCY: Cycle 2 1996–1997). 20,025 respondents representing 5,482,000 Canadian children and youth age 0–13 years were asked about the occurrence of an injury within the preceding 12 months. 19,939/20,025 (99.7%) responded with 2011 (10.2%) reporting at least one injury within the preceding 12 months. Within this group sustaining injuries, 69 (0.2%) representing 13,100 Canadian children and youth reported sustaining a concussion within the preceding year. The vast majority of these concussions occurred at home as a result of a fall or sports-related injury (75%), and over half (61%) occurred at home or school/daycare. Confirming that these were significant injuries, respondents were seen by their family physician on average one more time (3.5 vs. 2.2, p<0.01) and were more likely to be hospitalized overnight (OR=8.1, 95% CI: 4.3, 15.1, p<0.001). Children and youth who sustained a concussion were described as more physically active than others (p=0.002). Age, sex, and attention deficit disorder, among other factors did not appear to be associated with reporting concussion (all p>0.05). No remarkable profile of Canadian children and youth who sustain concussion emerged from our examination of the NLSCY data. Children with reports of concussion appear to be more (but not hyper) active.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".