Selective serotonin reuptake inhibitors increase risk of upper gastrointestinal bleeding when used with NSAIDs: a systemic review and meta-analysis
Bibliographic record
Abstract
The use of selective serotonin reuptake inhibitors (SSRIs) can increase the risk of gastrointestinal (GI) bleeding. Similarly, it is well known that the use of NSAIDs predisposes patients to upper GI bleeding. The aim of this study was to explore if the addition of SSRIs in patients already taking NSAIDs significantly increases their risk for upper GI bleeding. An electronic literature search was conducted using the SCOPUS and MEDLINE databases from inception through September 2020. Cohort and case-control trials that reported patients with upper GI bleeding on NSAIDs with SSRIs, compared to controls on NSAIDs only were included. Newcastle-Ottawa checklist was used to ensure inclusion of high-quality studies. Data was extracted by the lead investigator and cross-checked by the second author. Dichotomous data was pooled to obtain an odds ratio (OR) of the risk of upper GI bleeding in patients on NSAIDs with concomitant SSRI use. The primary endpoint of the study was the risk of upper GI bleeding with SSRIs and NSAIDs compared to NSAIDs alone. A total of 366 citations were reviewed, and 21 were selected for full-text evaluation. 1 cohort and 9 case-control studies were eligible. There was an additional increased risk of upper GI bleeding in patients on NSAIDs with concomitant SSRI use (OR 1.75, 95% CI = 1.32-2.33). In patients already on NSAID therapy, the concomitant use of SSRIs can significantly increase the risk of upper of GI bleeding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".