Variations in rates of appropriate and inappropriate carotid endarterectomy for stroke prevention in 4 Canadian provinces
Bibliographic record
Abstract
BACKGROUND: Carotid endarterectomy (CE), when performed on appropriate patients, reduces the incidence of stroke, yet there are marked variations in rates of this procedure. We sought to determine reasons for the variation in CE rates in 4 Canadian provinces. METHODS: We identified all CEs performed in 4 Canadian provinces between January 2000 and December 2001, inclusive. From chart review and expert assessment, we determined the proportion of these procedures that were appropriate, inappropriate or of uncertain appropriateness, using the RAND/UCLA Appropriateness Method. We sought to determine the variation in rates by province and whether the variation was due to differences in type of hospital, surgical specialty or surgical volume. RESULTS: Overall, 1656 (52.3%) of the 3167 CEs studied were performed for appropriate indications. The proportions of appropriate procedures were 78.2% (176/225) in Saskatchewan, 58.7% (481/819) in Alberta, 49.1% (350/713) in Manitoba and 46.0% (649/1410) in British Columbia (p < 0.001 across provinces). Rates of appropriate procedures per 100 000 population ranged from 44.3 in Manitoba to 16.2 in Saskatchewan (p < 0.001 across provinces). CEs were more likely to be appropriate when performed by a neurosurgeon compared with all other surgeons (74.4% v. 49.4% were appropriate; p < 0.001), when performed by surgeons doing fewer than 31 procedures over 2 years compared with surgeons doing more than 31 (70.1% v. 49.5% were appropriate; p < 0.001) and when performed in hospitals doing fewer than 135 procedures per year compared with hospitals doing more than 135 (63.4% v. 49.1% were appropriate; p < 0.001). Overall, 10.3% of procedures were done for inappropriate reasons. INTERPRETATION: Our findings suggest some overuse (for inappropriate or uncertain indications) but also some underuse (low population rates in some regions). High rates of CE are associated with lower rates of appropriateness for both surgeons and hospitals. That 1 in 10 CEs is done inappropriately suggests the need for preoperative assessment of 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".