Towards valid ‘serious non-fatal injury’ indicators for international comparisons based on probability of admission estimates
Bibliographic record
Abstract
BACKGROUND: Governments wish to compare their performance in preventing serious injury. International comparisons based on hospital inpatient records are typically contaminated by variations in health services utilisation. To reduce these effects, a serious injury case definition has been proposed based on diagnoses with a high probability of inpatient admission (PrA). The aim of this paper was to identify diagnoses with estimated high PrA for selected developed countries. METHODS: The study population was injured persons of all ages who attended emergency department (ED) for their injury in regions of Canada, Denmark, Greece, Spain and the USA. International Classification of Diseases (ICD)-9 or ICD-10 4-digit/character injury diagnosis-specific ED attendance and inpatient admission counts were provided, based on a common protocol. Diagnosis-specific and region-specific PrAs with 95% CIs were calculated. RESULTS: The results confirmed that femoral fractures have high PrA across all countries studied. Strong evidence for high PrA also exists for fracture of base of skull with cerebral laceration and contusion; intracranial haemorrhage; open fracture of radius, ulna, tibia and fibula; pneumohaemothorax and injury to the liver and spleen. Slightly weaker evidence exists for cerebellar or brain stem laceration; closed fracture of the tibia and fibula; open and closed fracture of the ankle; haemothorax and injury to the heart and lung. CONCLUSIONS: Using a large study size, we identified injury diagnoses with high estimated PrAs. These diagnoses can be used as the basis for more valid international comparisons of life-threatening injury, based on hospital discharge data, for countries with well-developed healthcare and data collection systems.
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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 | 0.163 | 0.276 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".