Abstract 23083: Glycemic Therapies, Atherothrombotic And Heart Failure Risk, And Outcomes By Baseline Cardiovascular Disease Status: Meta-analysis of Large CV Outcome Trials
Bibliographic record
Abstract
Purpose/Background: Recent glycemic therapy CV outcome trials have demonstrated risk reduction on the composite of CV death, MI or stroke (atherothrombotic MACE) and heart failure (HF). We investigated whether effects are modified by weight change, an indirect marker of intravascular volume status and explored among effective therapies heterogeneity by baseline CVD status. Methods: Random-effects metaanalysis among glycemic therapy CV outcome trials on the relative risk (RR) of MACE and HF. Results: Among 21 trials of 149,916 patients, 5,749 (3.8%) HF and 15,357 (10.2%) MACE events occurred during a mean 3.9 years of follow-up (range 1.5-10 years). Across classes of glycemic therapy there was substantial heterogeneity for the risk of HF (I 2 =74%; P-interaction <0.0001) but not MACE (I 2 =31%; P-interaction=0.08). Among effective therapies, reduction in MACE risk was confined to patients with established CVD (HR 0.82, 95% CI, 0.77-0.89) with no effect among patients without CVD (HR 1.06, 95% CI, 0.86-1.31; P-interaction=0.02; Figure). Meta-regression showed that every 1 kg change in weight between therapies modified the RR for HF by 4.8% (95% CI 2.5-7.0; P=0.00002). Conclusion: An effect on MACE appears confined to secondary prevention whereas an effect on HF appears related to achieved weight change between glycemic therapies.
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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 | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.043 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".