Monitoring for antidepressant-associated adverse events in the treatment of patients with major depressive disorder: An international consensus statement
Bibliographic record
Abstract
OBJECTIVES: These recommendations were designed to ensure safety for patients with major depressive disorder (MDD) and to aid monitoring and management of adverse effects during treatment with approved antidepressant medications. The recommendations aim to inform prescribers about both the risks associated with these treatments and approaches for mitigating such risks. METHODS: Expert contributors were sought internationally by contacting representatives of key stakeholder professional societies in the treatment of MDD (ASBDD, CANMAT, WFSBP and ISAD). The manuscript was drafted through iterative editing to ensure consensus. RESULTS: Adequate risk assessment prior to commencing pharmacotherapy, and safety monitoring during pharmacotherapy are essential to mitigate adverse events, optimise the benefits of treatment, and detect and assess adverse events when they occur. Risk factors for pharmacotherapy vary with individual patient characteristics and medication regimens. Risk factors for each patient need to be carefully assessed prior to initiating pharmacotherapy, and appropriate individualised treatment choices need to be selected. Some antidepressants are associated with specific safety concerns which were addressed. CONCLUSIONS: Risks of adverse outcomes with antidepressant treatment can be managed through appropriate assessment and monitoring to improve the risk benefit ratio and improve clinical outcomes.
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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 | 0.033 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".