Comparison of routine blood alcohol tests and ICD-10-AM coding of alcohol involvement for major trauma patients
Why this work is in the frame
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Bibliographic record
Abstract
Background: Alcohol use is a key preventable risk factor for serious injury. To effectively prevent alcohol-related injuries, we rely on the accurate surveillance of alcohol involvement in injury events. This often involves the use of administrative data, such as International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) coding. Objective: To evaluate the completeness and accuracy of using administrative coding for the surveillance of alcohol involvement in major trauma injury events by comparing patient blood alcohol concentration (BAC) with ICD-10-AM coding. Method: This retrospective cohort study examined 2918 injury patients aged ≥18 years who presented to a major trauma centre in Victoria, Australia, over a 2-year period, of which 78% ( n = 2286) had BAC data available. Results: While 15% of patients had a non-zero BAC, only 4% had an ICD-10-AM code suggesting acute alcohol involvement. The agreement between blood alcohol test results and ICD-10-AM coding of acute alcohol involvement was fair ( κ = 0.33, 95% confidence interval: 0.27–0.38). Of the 341 patients with a non-zero BAC, 82 (24.0%) had ICD-10-AM codes related to acute alcohol involvement. Supplementary factors Y90 Evidence of alcohol involvement determined by blood alcohol level codes, which specifically describe patient BAC, were assigned to just 29% of eligible patients with a non-zero BAC. Conclusion: ICD-10-AM coding underestimated the proportion of alcohol-related injuries compared to patient BAC. Implications: Given the current role of administrative data in the surveillance of alcohol-related injuries, these findings may have significant implications for the implementation of cost-effective strategies for preventing alcohol-related injuries.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it