Validation of Diagnosis and Procedure Codes for Revascularization for Peripheral Artery Disease in Ontario Administrative Databases
Bibliographic record
Abstract
PURPOSE: To estimate the positive predictive value of diagnosis and procedure codes for open and endovascular revascularization for peripheral artery disease (PAD) in Ontario administrative databases. METHODS: We conducted a retrospective validation study using population-based Ontario administrative databases (2005-2019) to identify a random sample of 600 patients who underwent revascularization for PAD at two academic centres, based on ICD-10 diagnosis codes and Canada Classification of Health Intervention procedure codes. Administrative data coding was compared to the gold standard diagnosis (PAD vs. non-PAD) and revascularization approach (open vs. endovascular) extracted through blinded hospital chart re-abstraction. Positive predictive values and 95% confidence intervals were calculated. Combinations of procedure codes with or without supplemental physician claims codes were evaluated to optimize the positive predictive value. RESULTS: The overall positive predictive value of PAD diagnosis codes was 87.5% (84.6%-90.0%). The overall positive predictive value of revascularization procedure codes was 94.3% (92.2%-96.0%), which improved through supplementation with physician fee claim codes to 98.1% (96.6%-99.0%). Algorithms to identify individuals revascularized for PAD had combined positive predictive values ranging from 82.8% (79.6%-85.8%) to 95.7% (93.5%-97.3%). CONCLUSION: Diagnosis and procedure codes with or without physician claims codes allow for accurate identifi-cation of individuals revascularized for PAD in Ontario administrative databases.
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How this classification was reachedexpand
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 | 0.000 | 0.003 |
| 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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".