Geographic Variation in the Rate of Carotid Endarterectomy in Canada
Bibliographic record
Abstract
UNLABELLED: Background and Purpose-- Carotid endarterectomy (CEA) is an important method of stroke prevention, but its usage in Canada is not well known. The indications for CEA have been well informed by the recent clinical trials, but the impact of this information on the rate and regional variation in the rate of CEA is unknown. This study sought to determine the rate and the regional variation in the rate of CEA in Canada, its provinces, and census divisions for 1994-1997. METHODS: Discharge data from all hospitals in Canada except Quebec were obtained from the Canadian Institute for Health Information for 1994-1997 and were searched for CEA by residential site. Rates and variations in rates were calculated. RESULTS: The national age- and sex-adjusted rate per 100 000 people of CEA for those aged >/=40 years rose from 31.7 in 1994 to 40.5 in 1997. Provincial rates in 1997 varied from a low of 25.7 in Saskatchewan to high of 82.8 in Prince Edward Island. The census division rates varied even more, from a low of 0 in several divisions to a high of 179. CONCLUSIONS: The recent slight increase in CEA rates may reflect the release of new efficacy results for CEA, especially for asymptomatic carotid stenosis, but the rates are still far below US levels. The marked regional variation in rates may reflect differing views on the appropriateness of indications such as asymptomatic carotid stenosis for CEA and the inconsistency of published clinical practice guidelines.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".