What to do if an initial antidepressant fails?
Bibliographic record
Abstract
OBJECTIVE: To provide family physicians with practical ways of managing depressed patients responding insufficiently to initial antidepressant treatment. QUALITY OF EVIDENCE: A search of MEDLINE and relevant bibliographies showed most studies could be categorized as level III evidence. Few well controlled studies (eg, level I evidence) specify treatment of next choice in rigorously defined treatment-refractory depression (TRD). MAIN MESSAGE: Failure to achieve and sustain full symptom remission affects relatively few treated depressed patients. Most chronically depressed people are not absolutely resistant but are relatively resistant to treatment; they fail to achieve the goals of treatment because of improper diagnosis or insufficient treatment application. The literature on TRD has largely focused on medication strategies; fewer studies investigated psychosocial approaches. The best established augmentation strategies are lithium salts and triidothyronine (T3). Combination antidepressants have become clinical psychiatrists' preferred treatment, despite limited evidence. Electroconvulsive therapy (ECT) remains a feasible option for TRD, but response rates are poor among people with TRD. High relapse rates after ECT remain a serious and common clinical dilemma. CONCLUSION: Family physicians should familiarize themselves with some new strategies to modify inadequate response to initial antidepressant treatment.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".