Association between Antidepressant Use and Prescribing of Gastric Acid Suppressants
Bibliographic record
Abstract
OBJECTIVE: To determine whether an increased risk of gastrointestinal events is present in younger, generally healthy adults who consume antidepressants. METHOD: We performed a retrospective cohort study using the pharmacy records of Canadian Forces (CF) members who received antidepressants between June 1997 and November 2002, excluding those taking bupropion for smoking cessation. The control cohort comprised members who received salbutamol. Changes in use of gastric acid-reducing agents (GARs) and incident GAR prescribing rates were compared pre- and postinitiation of target medications. We performed ogistic regression analyses to evaluate the effects of age, sex, and concomitant medication use on GAR prescribing. RESULTS: A total of 8722 antidepressant exposures were identified among 5588 CF members. The control cohort consisted of 3059 people with 4154 salbutamol exposures. The number of incident GAR prescriptions decreased in both groups postexposure; however, the rate of decrease was significantly greater among salbutamol users (odds ratio 1.38; 95%CI, 1.12 to 1.71). Antidepressant users were significantly more likely to receive a new prescription for GAR following both short-term and long-term exposure (adjusted odds ratio 4.93; 95%CI, 2.66 to 9.21 and 2.83; 95%CI, 2.05 to 3.92, respectively). Antiplatelet agents, bisphosphonates, oral corticosteroids, and nonsteroidal antiinflammatory drugs were significant predictors of GAR prescription. CONCLUSION: Consistent with other reports, this study has identified that antidepressant use increases the risk for use of a gastric acid suppressant. Careful consideration should thus be made with regard to increased gastric event risk among antidepressant users.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".