Individualized antidepressant therapy in patients with major depressive disorder
Bibliographic record
Abstract
OBJECTIVE: To introduce a visual clinical decision support tool to assist with individualizing first-line antidepressant pharmacotherapy for adults with major depressive disorder (MDD) in a Canadian context. SOURCES OF INFORMATION: . MAIN MESSAGE: Major depressive disorder affects about 4.7% of Canadians annually and is a prevalent condition encountered and diagnosed in primary care. Untreated depression is associated with decreased quality of life, increased risk of suicide, and worsening physical health outcomes when depression co-occurs with other chronic medical conditions. In a network meta-analysis, antidepressant medications (such as selective serotonin reuptake inhibitors, serotonin-norepinephrine reuptake inhibitors, bupropion, and vortioxetine) reduced depressive symptoms by 50% or more when compared with placebo in acute treatment of adults with moderate to severe MDD. Poor treatment adherence and high discontinuation rates limit MDD treatment success. Factors such as strong therapeutic alliances between patients and prescribers, collaborative care, patient education, and supportive self-management have been shown to enhance treatment adherence. The most recent Canadian Network for Mood and Anxiety Treatments depression treatment guidelines (published in 2016) suggest 15 different first-line antidepressant medication options for the treatment of MDD. There is a need for evidence-informed decision support aids to individualize antidepressant therapy to treat patients diagnosed with MDD. CONCLUSION: Recent studies on antidepressants have indicated no single antidepressant is superior to others in treating patients with MDD. This suggests there may be opportunities to enhance treatment adherence and success by tailoring antidepressant therapy to align with each patient's preferences. The Antidepressant Decision Support Tool was developed to help prescribers and adult patients engage in shared decision making to select an individualized and optimal first-line antidepressant for the treatment of acute MDD.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".