Who goes where? determining factors that influence where severely injured Canadian children are treated
Bibliographic record
Abstract
Background To date research has suggested that paediatric trauma systems are associated with a reduction in preventable deaths. However, there has been little work to determine what factors are associated with determining where a severely injured paediatric patient is treated. Aims/Objectives/Purpose To determine factors that are associated with where a paediatric patient with a severe injury receives definitive treatment. Treatment location will be classified by hospital type; paediatric trauma centre (level I/II), adult trauma centre (level I/II) or other. Methods The Discharge Abstract Database will be used to discern factors that are associated with where a severely injured child receives definitive treatment. Children (≤16 years) who have sustained a severe injury (defined by ICD-10 codes) will be isolated. The primary outcome variable will be treatment location classified into three groups by hospital type; paediatric trauma centre (level I/II), adult trauma centre (level I/II) or other. Demographic, hospital and other care related factors will be included in the final adjusted models Outcome Analysis is currently underway. Significance/Contribution This study will provide an overview of the current functioning of the regional Canadian paediatric trauma systems and what factors are related to definitive care. This will provide key information to address any disparities in access to proper trauma care for severely injured paediatric patients. Additionally, it will allow for future work to determine if where definitive treatment is received impacts on patient outcomes.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".