Interpersonal sensitivity and response to selective serotonin reuptake inhibitors in patients with acute major depressive disorder
Bibliographic record
Abstract
BACKGROUND: Patients with major depression often suffer from excessive interpersonal sensitivity, although it is not typically measured in antidepressant clinical trials. Preliminary evidence suggests selective serotonin reuptake inhibitors have the capacity to reduce interpersonal sensitivity. METHODS: This was a pooled analysis of data from 1709 patients in three randomized, double-blind, placebo-controlled trials of fluoxetine and paroxetine for acute major depressive disorder. Depressive symptoms were assessed with the Hamilton Depression Rating Scale. A factor from the Symptom Checklist was used to assess interpersonal sensitivity. Our outcome of interest was change from baseline scores at the last assessment (up to 8 or 12 weeks, depending on the trial). RESULTS: Both medications produced significantly greater reductions in interpersonal sensitivity relative to placebo. The effect of medication remained significant after controlling for depression improvement, which explained 18.5% of the variation in interpersonal sensitivity improvement among those treated with active medication. The effect of medication on depressive symptoms, relative to placebo, was not influenced by baseline interpersonal sensitivity. LIMITATIONS: The outcome measured interpersonal sensitivity over the last week, and the results do not necessarily reflect changes in long-standing, trait-like patterns of interpersonal sensitivity. Only two medications were studied. CONCLUSIONS: Selective serotonin reuptake inhibitors are effective at treating interpersonal sensitivity in acutely depressed patients. This appears to be a unique drug effect that is not only the result of depression improvement. Future clinical trials might benefit from assessing interpersonal sensitivity more routinely.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".