Validation of abdominal aortic aneurysm repair codes in Ontario administrative data
Bibliographic record
Abstract
PURPOSE: To determine the positive predictive values (PPV) of Ontario administrative data codes for the identification of open (OSR) and endovascular (EVAR) repairs of elective (eAAA) and ruptured (rAAA) abdominal aortic aneurysms. METHODS: We randomly identified 319 eAAA and rAAA repairs at two Toronto hospitals between April 2003 and March 2015, using administrative health data in Ontario, Canada. International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) codes I71.3 and I71.4, were used to identify rAAA and eAAA patients, respectively. A blinded retrospective chart review was conducted and served as the gold standard comparator. Re-abstracted records were compared to Canadian Classification of Health Interventions (CCI) and Ontario Health Insurance Plan (OHIP) codes in the Canadian Institute for Health Information Discharge Abstract Database (CIHI-DAD) and OHIP databases. We calculated the PPV and 95% confidence intervals (95% CI) of individual and combined procedure and billing codes for elective and ruptured OSR and EVAR (eOSR, eEVAR, rOSR, and rEVAR). RESULTS: Permutation of codes allowed identification of eOSR with 95% PPV (95% CI 88, 98), eEVAR with 96% PPV (95% CI 90, 99), rOSR with 87% PPV (95% CI 79, 93) and rEVAR with 91% PPV (95% CI 59, 100). CONCLUSIONS: Diagnostic, procedure and billing code combinations allow identification of eOSR, eEVAR, rOSR and rEVAR patients in Ontario administrative data with a high degree of certainty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".