PW 1238 Socio-economic status and ed visits for pediatric concussion
Bibliographic record
Abstract
Concussion has been a topic of interest recently, new research and guidelines are emerging around the world. In Ontario, the number of Emergency Department (ED) visits for concussion is rising, but little is known about the association between concussion and socio-economic status. The objective of this study was to examine the association between socio-economic status and ED visits for concussions in Ontario, Canada. This is a longitudinal population-based study using routinely-collected administrative data from the Institute for Clinical Evaluative Sciences. Data from all Emergency Department visits and hospitalizations are included. For the purposes of this study, all injuries coded using the ICD-10 CA code associated with concussion (S060) were included. The denominator used for this study was the number of children residing in Ontario in each age group. The rate per 1 00 000 children was calculated from 2008 to 2015. There were 5889 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 1 00 000 person years in 2008, and 7.12 for the highest socioeconomic class quintile. By comparison, the lowest and highest quintiles recorded 8.64 and 11.07 respectively in 2015. The rates of concussions among all socioeconomic quintiles were either stable or increasing. The results of this study suggest that rates of concussions are increasing among children. However, children in a higher income quintile consistently visited EDs for concussion more than children from lower income quintiles. This may be due to the increased opportunity wealthier children have to engage in organized sports. These results suggest that further policies related to awareness and identification of concussion need to be considered for all children.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".