Diabetes mellitus and Outcome after Successful Percutaneous Coronary Revascularization: The Mayo Clinic Experience 1979–1998
Bibliographic record
Abstract
Objective: To examine whether the outcome of patients after successful percutaneous coronary revascularization is influenced by diabetic status. Design: Retrospective analysis of the Mayo Clinic PTCA Registry. Materials and Methods: We analyzed the occurrence of all-cause death and death/myocardial infarction among diabetic (n = 2,155) and nondiabetic patients (n = 9,354) after successful percutaneous coronary revascularization at Mayo Clinic from October 1979 through December 1998. Results: Of the 11,509 patients who underwent percutaneous coronary revascularization during the study period, 2,155 were diabetic at the time of their index percutaneous coronary revascularization. The clinical success rate was similar for the two groups (≈87%). Among the patients with clinically successful interventions, the median (25th, 75th interquartiles) time of follow-up was 3.1 (1.1, 6.1) years for diabetic patients and 4.3 (2.0, 8.2) years for nondiabetic patients (p < 0.001). Diabetes mellitus was associated with increased adjusted risk (95% confidence interval) of death (1.73 [1.54, 1.94]) and death/infarction (1.62 [1.46, 1.79]). Conclusions: Although diabetic and nondiabetic patients had similar clinical success rates for percutaneous coronary revascularization, diabetic patients had significantly worse long-term outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".