Antidepressant Use and Risk of Venous Thromboembolism: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: Studies provided conflicting results on whether antidepressant use increased the risk of venous thromboembolism (VTE). Our aim was to examine the association between antidepressant use and the risk of VTE. METHODS: Pubmed, Embase, and the Cochrane Library were searched up to March 13, 2018. Case-control studies and cohort studies that examined the association between antidepressant use and the risk of VTE, deep vein thrombosis or pulmonary embolism were included. Several subgroup analyses and sensitivity analyses were conducted. GRADE approach was used to assess the quality of evidence. RESULTS: Nine studies (six case-control studies and three cohort studies) were included. Overall, antidepressant use may be associated with an increased risk of VTE (OR 1.27, 95% CI 1.09 to 1.49); however, no association was observed in studies with low risk of bias (OR 1.27, 95% CI 0.84 to 1.92). No association between selective serotonin reuptake inhibitor use and VTE risk was detected in the overall analysis (OR 1.10, 95% CI 0.90 to 1.34) and in subgroup analysis of studies with low risk of bias. Tricyclic antidepressant may be associated with an increased VTE risk (OR 1.26, 95% CI 1.02 to 1.57), and the quality of evidence was rated as very low by GRADE approach; however, no association was observed when we only included studies with low risk of bias. CONCLUSIONS: There was no association between selective serotonin reuptake inhibitor use and VTE risk. Tricyclic antidepressant may be associated with an increased VTE risk, but the quality of evidence was very low.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".