11.25 The association between socio-economic status and Emergency Department visits for concussion
Bibliographic record
Abstract
Objective To examine the association between socio-economic status and Emergency Department (ED) visits for concussions in children and youth in Ontario, Canada. Design Longitudinal population-based study using administrative data from all ED visits. Setting All ED visits in Ontario, Canada. Participants Children and youth residing in Ontario. Interventions (or Assessment of Risk Factors) The rate per 100000 children was calculated from 2008 to 2015. Socio-economic status was defined by a marginalization index and grouped into quintiles from the highest to the lowest. Comparisons were made over the 7-year period and by quintile. Outcome Measures ICD-10 diagnosis of concussion. Main Results There were 5,889 concussions reported at an emergency department in 2008, and 14,906 in 2015. The rate among the lowest socioeconomic class quintile was 5.23 per 100000 person years in 2008, and 7.12 for the highest socioeconomic class quintile, and 8.64 and 11.07 respectively in 2015. Conclusions Rates of ED visits for concussions increased among children over time. However, children in higher income quintiles consistently visited EDs for concussion more than children from lower quintiles. This may be due to the increased opportunity wealthier children have to engage in contact sports such as hockey and football or may reflect differences in the likelihood of seeking care. Policies related to awareness and identification of concussions need to be considered for all children and may need to be improved for those in poorer areas.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".