Administrative Hospitalization Database Validation of Cardiac Procedure Codes
Bibliographic record
Abstract
BACKGROUND: Although cardiac procedures are commonly used to treat cardiovascular disease, they are costly. Administrative data sources could be used to track cardiac procedures, but sources of such data have not been validated against clinical registries. OBJECTIVES: To examine accuracy of cardiac procedure coding in administrative databases versus a prospective clinical registry. SAMPLE: We examined a total of 182,018 common cardiac procedures including percutaneous coronary intervention (PCI), coronary artery bypass graft (CABG) surgery, valve surgery, and cardiac catheterization procedures during fiscal years 2005 and 2006 across 18 cardiac centers in Ontario, Canada. RESEARCH DESIGN: Accuracy of codes in the Canadian Institute for Health Information (CIHI) administrative databases were compared with the clinical registry of the Cardiac Care Network. RESULTS: Comparing 17,511 CIHI and 17,404 registry procedures for CABG surgery, the positive predictive value (PPV) of CIHI-coded CABG surgery was 97%. In 6229 CIHI-coded and 5885 registry-coded valve surgery procedures, the PPV of the administrative data source was 96%. Comparing 38,527 PCI procedures in CIHI to 38,601 in the registry, the PPV of CIHI was 94%. Among 119,751 CIHI-coded and 111,725 registry-coded cardiac catheterization procedures, the PPV of administrative data was 94%. When the procedure date window was expanded from the same day to ±1 days, the PPV was 96% (PCI) and exceeded 98% (CABG surgery), 97% (valve surgery), and 95% (cardiac catheterization). CONCLUSIONS: Using a clinical registry as the gold standard, the coding accuracy of common cardiac procedures in the CIHI administrative database was high.
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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 | 0.014 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".