Additional file 2 of Efficacy of cardiometabolic drugs in reduction of epicardial adipose tissue: a systematic review and meta-analysis
Bibliographic record
Abstract
Additional file 2: Table S1. Quality assessment with Newcastle-Ottawa scale of the included studies. Figure S1. Forest plots of the GLP-1 agonists and SGLT-2 inhibitor's effect on epicardia adipose tissue (EAT) reduction within 3 months of follow-up. Figure S2. Forest plots of the GLP-1 agonists and SGLT-2 inhibitor's effect on EAT reduction within 6 months of follow-up. Figure S3. Forest plots of the GLP-1 agonists and SGLT-2 inhibitor's effect on EAT thickening reduction measured by ultrasound within 3 months of follow-up. Figure S4. Forest plots of the GLP-1 agonists and SGLT-2 inhibitor's effect on epicardia adipose tissue thickening reduction measured by ultrasound within 6 months of follow-up. Figure S5. Funnel plot of effect size versus standard error for the overall effect of cardiometabolic drugs on EAT thickening. Dots represent the single studies while diamonds are the overall standardized mean difference (Std diff in means). White filling refers to real studies while black fill relates to Duval and Tweedie’s trim and fill method. Figure S6. Meta-regression analysis. Impact of HbA1c on the difference in composite cardiometabolic drugs effect and EAT reduction (A), and cardiometabolic drugs effect and EAT thickness reduction measured ultrasound (B). Impact of cholesterol low-density lipoprotein levels (LDL-C) on the difference in composite cardiometabolic drugs effect and EAT reduction (C), and cardiometabolic drugs effect and EAT thickness reduction measured ultrasound (D). Impact of male sex on the difference in composite cardiometabolic drugs effect and EAT reduction (E), and cardiometabolic drugs effect and EAT thickness reduction measured ultrasound (F).
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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 | 0.005 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.773 | 0.031 |
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".