Indicators of injury burden: Which types are the most important?
Bibliographic record
Abstract
Injury indicators are used for monitoring the impact of injury prevention initiatives on the population burden of injury. The object of the present study was to identify the types of injury responsible for the major component of the population health burden of injury in a large cohort in Manitoba, Canada. Injury cases (ICD-9-CM 800-995) aged 18-64 years were identified from all Manitoba hospital data between 1988 and 1991. Morbidity data were obtained from hospital discharge abstracts 12 months prior to date of injury and for 12 months post-injury. Outcomes for individuals were calculated as the difference pre- and post-injury in hospital inpatient days. Death outcomes in the 12 months post-injury were obtained by linking the cohort with the population registry. Summed outcomes across the population were stratified into injury types based on the International Code of Diseases (ICD) code of the index injury. Outcomes were also stratified by injury severity score categories where the injury severity score was obtained using ICDMAP-90. When ranked by contribution to the cohort's cumulative hospital inpatient days in the 12 months post-injury, the six most common ICD subchapter groups accounted for 65% of the total inpatient days. These six injury types also accounted for 62% of the total number of deaths in this cohort in 12 months after injury. The suggested injury types to use as indicators of burden include fracture of the lower limb, fracture of the head and neck, poisonings, intracranial injury, fracture of the upper limb, and fracture of skull.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".