Safety of concomitant use of oral anticoagulants and antidiabetic drugs: a systematic review of observational studies
Bibliographic record
Abstract
INTRODUCTION: We systematically reviewed observational studies assessing the safety of concomitant use of oral anticoagulants (OACs) and antidiabetic drugs (ADs). METHODS: We systematically searched MEDLINE/PubMed and EMBASE up to 10/2024 for cohort, case-control, and case-only studies on concomitant use of OACs and ADs and the risk of adverse outcomes (hypoglycemia, bleeding). Risk of bias was assessed using the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool. RESULTS: = 1,370,036). Concomitant use of sulfonylureas and warfarin was mostly associated with increased risks of hypoglycemia versus sulfonylurea use alone (five studies); results were heterogeneous when comparing concomitant use of sulfonylureas and warfarin versus concomitant use of sulfonylureas and DOACs (two studies) and concomitant use of non-sulfonylurea ADs and warfarin versus non-sulfonylurea AD use alone (two studies). Concomitant use of warfarin and sulfonylureas was not associated with the risk of bleeding versus warfarin use alone (one study). Via ROBINS-I, four studies were at moderate, one at serious, and two at critical risk of bias. CONCLUSIONS: Given inconsistent findings and a non-negligible risk of bias, observational studies do not suggest major clinical hazards due to concomitant use of OACs and ADs. (PROSPERO registration: CRD42024505475).
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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 | 0.025 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".