Additional file 1 of Efficacy of antihyperglycemic therapies on cardiovascular and heart failure outcomes: an updated meta-analysis and meta-regression analysis of 35 randomized cardiovascular outcome trials
Bibliographic record
Abstract
Additional file 1: Table S1. Definition of heart failure and major adverse cardiovascular events of the included trials. Table S2. Excluded trials through detailed full-text assessment. Table S3. Risk of bias of the included trials. Table S4. Univariate meta-regression analyses of HbA1c reduction and the estimated log risk ratio of major adverse cardiovascular events based on intervention type. Table S5. Univariate meta-regression analyses of bodyweight change and the estimated log risk ratio of heart failure based on intervention type. Figure S1. Derivation of formula to obtain the relative risk ratio reduction of outcomes with meta-regression results. Figure S2. Funnel plots for assessing publication bias of major adverse cardiovascular events (MACE) and heart failure (HF) outcomes. Figure S3. Efficacy of antihyperglycemic therapies on the risk of major adverse cardiovascular events (MACE) in each subgroup. Figure S4. Association between the risk of major adverse cardiovascular events (MACE) and HbA1c reduction stratified by the baseline prevalence of ASCVD. Figure S5. Efficacy of antihyperglycemic therpies on the risk of heart failure (HF) in each subgroup. Figure S6. Association between heart failure (HF) risk and bodyweight change stratified by the baseline prevalence of ASCVD.
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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 | 0.006 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.777 | 0.038 |
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".