Incretin-based therapies in patients with Type 2 diabetes mellitus and renal impairment
Bibliographic record
Abstract
The objective of this thesis is to add to literature addressing the safety and effectiveness of incretin-based agents in patients with diabetes and chronic kidney disease. Methods: Two complementary studies were conducted, a systematic review and meta-analysis and a population-based cohort study. Results: The systematic review and meta-analysis demonstrated in patients with diabetes and moderate or severe chronic kidney disease (CKD), incretin-based therapies effectively reduced glycated hemoglobin compared to placebo (WMD -0.53; 95%CI -0.64, -0.42). The pooled relative risk (RR) for all-cause mortality indicated no evidence of effect for incretin vs. placebo (RR 1.02; 95%CI 0.50, 2.06). In the cohort study dipeptidyl peptidase-4 (DPP-4) inhibitors, as second-line therapy were not significantly associated with a reduction in all-cause mortality (adjusted hazard ratio 0.74 [95%CI 0.52-1.04] vs. SU initiators). Conclusion: The meta-analysis supports incretin-based therapies as effectively reducing glycemia without substantial increased risk of hypoglycemia. The cohort study demonstrated second-line DPP4 inhibitor therapy was not significantly associated with a reduction in all-cause mortality when compared to sulfonylureas. Despite their introduction to the pharmaceutical market in 2007, there is still much to be understood regarding incretin therapy.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".