Abstract 1: Did Carotid Stenting and Endarterectomy Outcomes Change Over Time in the Carotid Revascularization Endarterectomy versus Stenting Trial?
Bibliographic record
Abstract
Background The Carotid Revascularization Endarterectomy versus Stenting Trial (CREST) enrolled 2502 patients between December of 2000 and mid-July of 2008. We analyzed temporal changes in outcomes for both CEA and CAS over the course of this study. Methods Enrollment was divided into 3 consecutive epochs (5 years, 14 months, and 16 months), each with approximately 834 patients ( Table ). Rates for the primary outcome of death, stroke, and myocardial infarction (DSMI) and for death and stroke (DS) during the periprocedural period were calculated. Poisson regression was used to adjust rates for age, sex, dyslipidemia, and symptomatic status, all of which were found to influence outcomes. Results For CAS, there was a 26% decline in DSMI (6.2% → 4.6%) and a 35% decline in DS (5.5% → 3.6%). For both composite endpoints in CEA there was no consistent pattern ( Table ). As CREST progressed, it enrolled younger patients, more men, patients more likely to be dyslipidemic and enrolled more asymptomatic patients (all p < 0.05). Adjustment for these changes mediated the improvement in event rates for the CAS patients, and had no consistent effect on CEA event rates ( Table ). Conclusion Periprocedural safety for CAS improved over time in CREST. Changes were inconsistent for CEA. Improvements for CAS in part appear to reflect changes in patient selection related to age, sex, and risk factors, as adjustment for those variables attenuated the decline in rates. Linking rates to operator experience was not feasible because of the large number of new interventionalists and surgeons entering CREST over time.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".