Psychosocial and clinical predictors of response to pharmacotherapy for depression
Bibliographic record
Abstract
A more complete understanding of the psychosocial and clinical predictors of response to pharmacotherapy would be of great value to both patients and physicians. Most demographic and clinical factors have not been found to be useful predictors of response. Although comorbid illness affects quality of life, there is confounding evidence about its importance when predicting response to antidepressant therapy. Some social support factors appear to be positive predictors of outcome in most trials. There is evidence to suggest that comorbid anxiety disorders and panic-agoraphobic spectrum symptoms are negative predictors of response to treatment. Substance abuse has been associated with a poorer response to antidepressant therapy, and recovery from substance abuse problems has been shown to be poorer among patients with comorbid depression. Assessment of personality dimensions may be a useful predictor of clinical course and outcome, but personality disorders present a complicated picture, with significant interaction among variables. A number of variables are significantly related to clinical course, but few factors have been clearly linked to treatment response. The challenge is to determine if any of these factors are indeed independent predictors of response and whether it is possible to match choice of antidepressant therapy and patient type.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".