Socio Economic Status and Traumatic Brain Injury amongst Pediatric Populations: A Spatial Analysis in Greater Vancouver
Bibliographic record
Abstract
INTRODUCTION: Within Canada, injuries are the leading cause of death amongst children fourteen years of age and younger, and also one of the leading causes of morbidity. Low Socio Economic Status (SES) seems to be a strong indicator of a higher prevalence of injuries. This study aims to identify hotspots for pediatric Traumatic Brain Injury (TBI) and examines the relationship between SES and pediatric TBI rates in greater Vancouver, British Columbia (BC), Canada. METHODS: Pediatric TBI data from the BC Trauma Registry (BCTR) was used to identify all pediatric TBI patients admitted to BC hospitals between the years 2000 and 2013. Spatial analysis was used to identify hotspots for pediatric TBI. Multivariate analysis was used to distinguish census variables that were correlated with rates of injury. RESULTS: Six hundred and fifty three severe pediatric TBI injuries occurred within the BC Lower Mainland between 2000 and 2013. High rates of injury were concentrated in the East, while low rate clusters were most common in the West of the region (more affluent neighborhoods). A low level of education was the main predictor of a high rate of injury (OR = 1.13, 95% CI = 1.03-1.23, p-Value 0.009). CONCLUSION: While there was a clear relationship between different SES indicators and pediatric TBI rates in greater Vancouver, income-based SES indicators did not serve as good predictors within this region.
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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 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".