How well do anatomical-based injury severity scores predict health service use in the 12 months after injury?
Bibliographic record
Abstract
There is an acknowledged need for valid and reliable injury scores, suitable for use at the population level, which can accurately predict the long-term outcome of injury. The objective was to quantify the extent to which the abbreviated injury severity score (AIS) and the functional capacity index score (FCI) predict use of health services in the 12 months following an injury event. A cohort of injured people (ICD-9-CM 800-995) aged 18 - 64 years was identified from Manitoba hospital discharge abstracts from January 1988 to December 1991. For each member of the cohort whose injuries could be mapped to an abbreviated injury scale unique identifier, a maximum AIS (maxAIS) and a maximum FCI (maxFCI) were obtained. The cohort was linked with hospital discharge abstracts, physicians' claims and deaths from the population registry for the 12 months following injury. Negative binomial regression was used to model the relationships between the severity scores and the three outcome measures, while controlling for potential confounding variables. In total, 20 677 (97%) eligible cases were identified, of which 16 834 (81%) could be assigned a maxAIS and 15 823 (77%) a maxFCI. MaxAIS and maxFCI were significantly associated with total days in hospital following injury, but explained little of the variation in any of the health service use outcome variables (maxAIS, partial pseudo r2 ranging from < 0.001 to 0.041; and maxFCI, partial pseudo r2 ranging from < 0.001 to 0.018). It was concluded that anatomical damage is only partly responsible for long-term injury outcome. Additional variables would need to be included in predictive models of health outcomes of injury before these models could be reliable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".