Patient-centered Management of Type 2 Diabetes Mellitus Based on Specific Clinical Scenarios: Systematic Review, Meta-analysis and Trial Sequential Analysis
Bibliographic record
Abstract
INTRODUCTION: New antihyperglycemic medications have been proven to have cardiovascular (CV) and renal benefits in type 2 diabetes mellitus (T2DM); however, an evidence-based decision tree in specific clinical scenarios is lacking. MATERIALS AND METHODS: Systematic review and meta-analysis of randomized controlled trials (RCTs), with trial sequential analysis (TSA). Randomized controlled trial inclusion criteria were patients with T2DM from 1 of these subgroups: elderly, obese, previous atherosclerotic CV disease (ASCVD), previous coronary heart disease (CHD), previous heart failure (HF), or previous chronic kidney disease (CKD). Randomized controlled trials describing those subgroups with at least 48 weeks of follow-up were included. Outcomes: 3-point major adverse cardiovascular events (MACE), CV death, hospitalization due to HF, and renal outcomes. We performed direct meta-analysis with the number of events in the intervention and control groups in each subset, and the relative risk of the events was calculated. RESULTS: Sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide 1 receptor agonists (GLP-1 RA) were the only antihyperglycemic agents related to a reduction in CV events in different populations. For obese and elderly populations, GLP-1 RA were associated with benefits in 3-point MACE; for patients with ASCVD, both SGLT2i and GLP-1 RA had benefits in 3-point MACE, while for patients with CHD, only SGLT2i were beneficial. CONCLUSIONS: SGLT2i and GLP-1 RA reduced CV events in selected populations: SGLT2i led to a reduction in events in patients with previous CHD, ASCVD, and HF. GLP-1 RA led to a reduction in CV events in patients with ASCVD, elderly patients, and patients with obesity. Trial sequential analysis shows that these findings are conclusive. This review opens a pathway towards evidence-based, personalized treatment of T2DM. REGISTRATION: PROSPERO CRD42019132807.
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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 | 0.033 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".