Validity of vascular trauma codes at major trauma centres
Bibliographic record
Abstract
BACKGROUND: The use of administrative databases in vascular injury research has been increasing, but the validity of the diagnosis codes used in this research is uncertain. We assessed the positive predictive value (PPV) of International Classification of Diseases, tenth revision (ICD-10), vascular injury codes in administrative claims data in Ontario. METHODS: We conducted a retrospective validation study using the Canadian Institute for Health Information Discharge Abstract Database, an administrative database that records all hospital admissions in Canada. We evaluated 380 randomly selected hospital discharge abstracts from the 2 main trauma centres in Toronto, Ont., St.Michael's Hospital and Sunnybrook Health Sciences Centre, between Apr. 1, 2002, and Mar. 31, 2010. We then compared these records with the corresponding patients' hospital charts to assess the level of agreement for procedure coding. We calculated the PPV and sensitivity to estimate the validity of vascular injury diagnosis coding. RESULTS: The overall PPV for vascular injury coding was estimated to be 95% (95% confidence interval [CI] 92.3-96.8). The PPV among code groups for neck, thorax, abdomen, upper extremity and lower extremity injuries ranged from 90.8 (95% CI 82.2-95.5) to 97.4 (95% CI 91.0-99.3), whereas sensitivity ranged from 90% (95% CI 81.5-94.8) to 98.7% (95% CI 92.9-99.8). CONCLUSION: Administrative claims hospital discharge data based on ICD-10 diagnosis codes have a high level of validity when identifying cases of vascular injury. LEVEL OF EVIDENCE: Observational Study Level III.
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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 | 0.010 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".