Clinical guidelines for the treatment of depressive disorders. IV. Medications and other biological treatments.
Bibliographic record
Abstract
BACKGROUND: The Canadian Psychiatric Association and the Canadian Network for Mood and Anxiety Treatments partnered to produce clinical guidelines for psychiatrists for the treatment of depressive disorders. METHODS: A standard guidelines development process was followed. Relevant literature was identified using a computerized Medline search supplemented by review of bibliographies. Operational criteria were used to rate the quality of scientific evidence, and the line of treatment recommendations included consensus clinical opinion. This section, "Medications and Other Biological Treatments," is 1 of 7 articles that were drafted and reviewed by clinicians. Revised drafts underwent national and international expert peer review. RESULTS: Evidence-based recommendations are presented for 1) choosing an antidepressant, based on efficacy, tolerability, and safety; 2) the optimal use of antidepressants, including augmentation, combination, and switching strategies; 3) maintenance treatment; and 4) electroconvulsive therapy (ECT), light therapy, and additional somatic treatments. Evidence from metaanalyses is presented first, followed by conclusions from randomized controlled trials (RCTs) and, if appropriate, open-label data. CONCLUSIONS: There is significant evidence to support the role of selective serotonin reuptake inhibitors (SSRIs), novel agents, and classic agents in the treatment of major depressive disorder (MDD). There is also evidence to support the use of somatic treatments, including ECT and light therapy, for some patients with MDD. There is limited evidence for the use of specific medications to treat subtypes of MDD. There is emerging evidence to support augmentation and combination strategies for patients previously nonresponsive to medication.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.051 |
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".