Clinical Outcomes in Patients With Type 2 Diabetes Mellitus and Peripheral Artery Disease
Bibliographic record
Abstract
Background: Recent trials have identified anti–diabetes mellitus agents that lower major adverse cardiovascular event (MACE) rates, although some increase rates of lower-extremity amputation (LEA). Patients with peripheral artery disease (PAD) have greater incidence of diabetes mellitus and risk for LEA, prompting this investigation of clinical outcomes in patients with diabetes mellitus and PAD in the EXSCEL trial (Exenatide Study of Cardiovascular Event Lowering). Methods: EXSCEL evaluated the effects of once-weekly exenatide (a GLP-1 [glucagon-like peptide-1] receptor agonist) versus placebo on the rates of the primary composite MACE end point (cardiovascular death, myocardial infarction, or stroke) among patients with type 2 diabetes mellitus. In this post hoc analysis, we assessed the association of baseline PAD with rates of MACE, LEA, and the effects of exenatide versus placebo in patients with and without PAD. Results: EXSCEL included 2800 patients with PAD (19% of the trial population). These individuals had higher unadjusted and adjusted rates of MACE compared with patients without PAD (13.6% versus 11.4%, respectively) as well as a higher adjusted hazard ratio (adjusted hazard ratio, 1.13 [95% CI, 1.00–1.27]; P =0.047). Patients with PAD had higher all-cause mortality (adjusted hazard ratio 1.38 [95% CI, 1.20–1.60]; P <0.001) and more frequent LEA (adjusted hazard ratio 5.48 [95% CI, 4.16–7.22]; P <0.001). Patients treated with exenatide or placebo had similar rates of MACE and LEA, regardless of PAD status. Conclusions: EXSCEL participants with PAD had higher rates of all-cause mortality and LEA compared with those without PAD. There were no differences in MACE or LEA rates with exenatide versus placebo. Clinical Trial Registration URL: https://www.clinicaltrials.gov . Unique identifier: NCT01144338.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".