An audit tool for assessing the appropriateness of carotid endarterectomy
Bibliographic record
Abstract
BACKGROUND: To update appropriateness ratings for carotid endarterectomy using the best clinical evidence and to develop a tool to audit the procedure's use. METHODS: A nine-member expert panel drawn from all the Canadian Specialist societies that are involved in the care of patients with carotid artery disease, used the RAND Appropriateness Methodology to rate scenarios where carotid endarterectomy may be performed. A 9-point rating scale was used that permits the categorization of the use of carotid endarterectomy as appropriate, uncertain, or inappropriate. A descriptive analysis was undertaken of the final results of the panel meeting. A database and code were then developed to rate all carotid endarterectomies performed in a Western Canadian Health region from 1997 to 2001. RESULTS: All scenarios for severe symptomatic stenosis (70-99%) were determined to be appropriate. The ratings for moderate symptomatic stenosis (50-69%) ranged from appropriate to inappropriate. It was never considered appropriate to perform endarterectomy for mild stenosis (0-49%) or for chronic occlusions. Endarterectomy for asymptomatic carotid disease was thought to be of uncertain benefit at best. The majority of indications for the combination of endarterectomy either prior to, or at time of coronary artery bypass grafting were inappropriate. The audit tool classified 98.0% of all cases. CONCLUSIONS: These expert panel ratings, based on the best evidence currently available, provide a comprehensive and updated guide to appropriate use of carotid endarterectomy. The resulting audit tool can be downloaded by readers from the Internet and immediately used for hospital audits of carotid endarterectomy appropriateness.
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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 | 0.064 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".