Examining the efficacy of antidepressant pharmacotherapy for interpersonal sensitivity in patients with depressive disorders
Bibliographic record
Abstract
Interpersonal sensitivity is a transdiagnostic trait vulnerability factor for several psychiatric conditions, most notably atypical depression. Surprisingly little research attention has been devoted to pharmacotherapy for interpersonal sensitivity. We conducted a literature review of randomized controlled trials conducted over five decades to determine which antidepressant medications have demonstrated efficacy on the Hopkins Symptom Checklist interpersonal sensitivity factors. Our search focused on adult samples with unipolar depressive disorders. Compared to placebo, we found consistent evidence for the superiority of selective serotonin reuptake inhibitors and imipramine, as well as monoamine oxidase inhibitors (mainly phenelzine) specifically for patients with atypical or anxious depression. Mianserin did not appear to be effective in two trials. Phenelzine was superior to imipramine, but only for patients with atypical features, mirroring the trends observed for depression treatment effects in those trials. There was inconsistent evidence for the superiority of selective serotonin reuptake inhibitors over tricyclics (mainly imipramine), but no studies reported the converse. Some trials were underpowered with small sample sizes, or did not adequately report statistics. There was a notable absence of studies with newer antidepressants. Although interpersonal sensitivity clearly responds to serotonergic antidepressants, it is not clear from the existing literature if these are more effective than medications lacking such properties. Future research will need to carefully choose pharmacologic agents with nonoverlapping mechanisms to answer this question.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".