Strategies for Managing Depression Refractory to Selective Serotonin Reuptake Inhibitor Treatment: A Survey of Clinicians
Bibliographic record
Abstract
OBJECTIVE: To examine treatment practices in cases where selective serotonin reuptake inhibitors (SSRIs) are ineffective. METHODS: We surveyed 801 clinicians (including 630 psychiatrists) attending the Massachusetts General Hospital's annual psychopharmacology review course. Clinicians were presented with a vignette about a patient with depression who had responded partially to an SSRI and were asked to choose among various strategies available to manage this patient. RESULTS: Of those surveyed, 466 clinicians had been in practice a mean of 16.6 years (SD 10.7). Not all clinicians chose to answer every question. Among 455 respondents, 84% (n = 382) chose to increase the dose of the SSRI, 10% (n = 47) chose augmentation or combination, and 7% (n = 31) opted for switching agents. When asked to switch to another agent, 448 responded, of whom 52% (n = 235) chose a newer antidepressant, 34% (n = 152) chose another SSRI, 10% (n = 44) chose a tricyclic antidepressant (TCA), 2% (n = 8) chose a serotonin norepinephrine reuptake inhibitor (SNRI), 1% (n = 5) chose a monoamine oxidase inhibitor (MAOI), and 1% (n = 4) chose an undefined "other" agent. Among 445 respondents, bupropion was the most widely chosen augmenting agent (30%, n = 134), followed by lithium (22%, n = 98). West coast and Canadian clinicians preferred to switch to another SSRI rather than to a newer antidepressant. Canadian clinicians preferred lithium to bupropion as their first-choice augmenting agent, as did clinicians from academic settings. Clinicians from community, individual practice, or group settings favoured bupropion. More experienced clinicians preferred bupropion as a first-choice augmenter, whereas less experienced ones showed a slight preference for lithium. Canadian clinicians were more likely to use MAOIs as second-line agents. CONCLUSIONS: Clinicians in this sample often followed strategies different from those recommended in the literature. Bupropion may have an important role in augmentating treatment with SSRIs.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".