ODP240 Routine Glucose-lowering Therapies and Risk of Adverse Outcomes in COVID-19 Patients with Diabetes: a Network Meta-analysis
Bibliographic record
Abstract
Abstract Background We aimed to evaluate the mortality of eight glucose-lowering therapies for COVID-19 patients with diabetes prior to diagnosis of COVID-19. Methods We searched PubMed, Embase, Cochrane Central Register, Web of Science, and ClinicalTrials.gov through June 2021. COVID-19 patients with diabetes while receiving glucose-lowering therapies for at least 14 days prior to COVID-19 confirmed were included. The Newcastle Ottawa scale (NOS) was used to assess the risk of bias in nonrandomized studies. Bayesian network meta-analyses were performed. Results Eleven distinct observational studies (3,631,682 COVID-19 patients with diabetes mellitus) were included. Compared with insulin, DPP4i, secretagogues, glucosidase inhibitors, and thiazolidinediones, the incidence of adverse outcomes in diabetics who took SGLT2i was relatively lower: OR 0.30 (95% CrI 0.17-0.55); 0.42 (0.24-0.83); 0.43 (0.24-0.83); 0.32 (0.16-0.70); 0.47 (0.23-0.95). The SUCRA value of SGLT2i was the lowest (1.8%), followed by GLPIRA (22.1%) and biguanides (33.3%). Conclusion SGLT2I may be an optimal choice for diabetics before COVID-19 infection. GLP1RA and guanidine can also be a good choice for the protection of diabetics during COVID-19 pandemic times. Presentation: No date and time listed
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.049 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".