Validation of Carotid Artery Revascularization Coding in Ontario Health Administrative Databases
Bibliographic record
Abstract
PURPOSE: The positive predictive value (PPV) of carotid endarterectomy (CEA) and carotid artery stenting (CAS) procedure and post-operative complication coding were assessed in Ontario health administrative databases. METHODS: Between 1 April 2002 and 31 March 2014, a random sample of 428 patients were identified using Canadian Classification of Health Intervention (CCI) procedure codes and Ontario Health Insurance Plan (OHIP) billing codes from administrative data. A blinded chart review was conducted at two high-volume vascular centers to assess the level of agreement between the administrative records and the corresponding patients' hospital charts. PPV was calculated with 95% confidence intervals (CIs) to estimate the validity of CEA and CAS coding, utilizing hospital charts as the gold standard. Sensitivity of CEA and CAS coding were also assessed by linking two independent databases of 540 CEA-treated patients (Ontario Stroke Registry) and 140 CAS-treated patients (single-center CAS database) to administrative records. RESULTS: PPV for CEA ranged from 99% to 100% and sensitivity ranged from 81.5% to 89.6% using CCI and OHIP codes. A CCI code with a PPV of 87% (95% CI, 78.8-92.9) and sensitivity of 92.9% (95% CI, 87.4-96.1) in identifying CAS was also identified. PPV for post-admission complication diagnosis coding was 71.4% (95% CI, 53.7-85.4) for stroke/transient ischemic attack, and 82.4% (95% CI, 56.6-96.2) for myocardial infarction. CONCLUSIONS: Our analysis demonstrated that the codes used in administrative databases accurately identify CEA and CAS-treated patients. Researchers can confidently use administrative data to conduct population-based studies of CEA and CAS.
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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 | 0.011 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".