Assessing effectiveness of treatment of depression in primary care
Bibliographic record
Abstract
BACKGROUND: There is a mismatch between the wish of a patient with depression to have counselling and the prescription of antidepressants by the doctor. AIMS: To determine whether counselling is as effective as antidepressants for depression in primary care and whether allowing patients to choose their treatment affects their response. METHOD: A partially randomised preference trial, with patients randomised to either antidepressants or counselling or given their choice of either treatment. The treatment and follow-up were identical in the randomised and patient preference arms. RESULTS: There were 103 randomised and 220 preference patients in the trial. We found: no differences in the baseline characteristics of the randomised and preference groups; that the two treatments were equally effective at 8 weeks, both for the randomised group and when the randomised and patient preference groups for a particular treatment were combined; and that expressing a preference for either treatment conferred no additional benefit on outcome. CONCLUSIONS: These data challenge several assumptions about the most appropriate treatment for depression in a primary care setting.
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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 | 0.021 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".