Coding accuracy of abdominal aortic aneurysm repair procedures in administrative databases – a note of caution
Bibliographic record
Abstract
BACKGROUND: Administrative databases have been used to compare methods used for abdominal aortic aneurysm (AAA) repair. This requires the use of procedural codes whose accuracy has not been established. In this study we measured the accuracy of procedural codes for open AAA repair and endovascular aneurysm repair (EVAR) in administrative databases. METHODS: Between April 2000 and July 2005, we identified all surgeries of non-ruptured AAA using open or EVAR technique at a tertiary-care teaching hospital. During the same time period, we identified all patients who were coded with either an open AAA repair or EVAR. RESULTS: During the study period, 514 people had an elective AAA repair or were coded with one. Coding quality of open AAA repair was poor (sensitivity 48.1%; specificity 77.4%; accuracy 52.9%) while that for EVAR was slightly better (sensitivity 58.2%; specificity 100%; accuracy 93.6%). We developed an algorithm that included similar procedures and considered anaesthetic type to improve the identification of both open repair (sensitivity 97.7%; specificity 86.9%; accuracy 95.9%) and EVAR (sensitivity 84.8%; specificity 99.5%; accuracy 97.3%). CONCLUSION: Administrative database codes that are routinely used to identify open AAA repairs or EVARs are inaccurate. However, slight modifications to the coding algorithms permit the use of administrative databases to study AAA repair.
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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 | 0.179 | 0.487 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".