Empirical validation of the New Zealand serious non-fatal injury outcome indicator for ‘all injury’
Bibliographic record
Abstract
Our purpose was to empirically validate the official New Zealand (NZ) serious non-fatal 'all injury' indicator. To that end, we aimed to investigate the assumption that cases selected by the indicator have a high probability of admission. Using NZ hospital in-patient records, we identified serious injury diagnoses, captured by the indicator, if their diagnosis-specific survival probability was ≤0.941 based on at least 100 admissions. Corresponding diagnosis-specific admission probabilities from regions in Canada, Denmark and Greece were estimated. Aggregate admission probabilities across those injury diagnoses were calculated and inference made to New Zealand. The admission probabilities were 0.82, 0.89 and 0.90 for the regions of Canada, Denmark and Greece, respectively. This work provides evidence that the threshold set for the official New Zealand serious non-fatal injury indicator for 'all injury' captures injuries with high aggregate admission probability. If so, it is valid for monitoring the incidence of serious injuries.
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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 | 0.044 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".