Comparing angiographic coronary revascularization strategies: a 'natural' experiment.
Bibliographic record
Abstract
OBJECTIVE: To study the impact of intracoronary stents on clinical restenosis in the 'real world'. DESIGN: Retrospective comparison of the rates of clinical restenosis between two cohorts exposed to different strategies for percutaneous transcatheter intervention. The endpoint was the first of death, myocardial infarction, coronary artery bypass grafting, repeat percutaneous transluminal coronary angioplasty (PTCA) or repeat coronary angiography within nine months. SETTING: Tertiary care cardiac referral centre serving a large, metropolitan population. PATIENTS: Patients undergoing angiographic revascularization from January 1 to February 28, 1996 (the 'restricted' group [R], n=147) were compared with a before and after cohort (the 'usual' group [U], n=232, divided into those who underwent revascularization between November 1 and November 30, 1995, and those who underwent revascularization between April 1 and May 31, 1996). INTERVENTIONS: The R group was revascularized during a period of economic constraint, which imposed a shortage on stent availability. The U cohort underwent revascularization before and after the shortage (an 'unrestricted' environment for stent usage). MAIN RESULTS: There was no difference in clinical restenosis rates between the R (34.7%) and U (37.9%) groups (P=0.524, OR R/U=0.915, 95% CI 0.694 to 1.206). Also, the rate of clinical restenosis was the same among patients who underwent PTCA without stent insertion (34.8%) and those who received a stent (39.4%) (P=0.368, OR=1.13, 95% CI 0.87 to 1.44). CONCLUSIONS: At the authors' institution, a restricted stenting policy did not result in a higher clinical restenosis rate than that of usual practice.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".