Bibliographic record
Abstract
The efficacy of carotid endarterectomy (CE) to pre-vent stroke is well established.1–4 Clinical trials haveshown that CE reduces the 5-year risk of stroke by 16.0 % when performed because of symptomatic lesions causing more than 70 % stenosis.5 The risk reduction is more modest (4.6 % and 5.9%, respectively) in cases of symptomatic moderate (50 % to 69%) stenosis or asympto-matic stenosis (> 60%).4,5 However, concerns remain re-garding the effectiveness of the procedure outside of clini-cal trials, when the potential benefit may be reduced.6,7 Although national societies have issued guidelines on indi-cations for CE,8,9 in some cases CE is performed on pa-tients who do not meet these guidelines. The RAND/UCLA (University of California at Los Angeles) Appropriateness Method,10,11 developed in re-sponse to concerns about possible unnecessary use of pro-cedures, is perhaps the most respected approach to defin-ing appropriate care, combining best evidence and expert opinion.12 The first study of the appropriateness of CE, published in 1988, showed that only one-third of pro-cedures were appropriate.13 A Canadian study in 1997 showed similar results.14 The role of health system factors in choosing patients appropriately for CE is not well ex-plored. Administrative databases allow only limited appre-ciation of the decision-making process that leads to the operating room. Our objectives were to describe the variation in appro-priateness of CE in 4 Canadian provinces, to document rates of appropriate CE in the provinces and to explore po-tential explanatory factors, such as hospital type, surgeon specialty and number of CEs performed each year per sur-geon and per hospital. Methods
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 | 0.127 | 0.409 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.032 | 0.027 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.031 | 0.021 |
| Insufficient payload (model declined to judge) | 0.171 | 0.080 |
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".