The Course of Depressive Illness in General Practice
Bibliographic record
Abstract
OBJECTIVE: Depression is reported to be common in primary care settings and to have a high likelihood of relapse during the 4- to 6-month period following initial symptomatic improvement. However, most prospective studies of long-term treatment of depression have been conducted with patients selected for participation in placebo-controlled drug protocols or psychiatric clinics associated with tertiary referral centres. METHOD: We examined the treatment course and outcome of outpatients with major depressive episode treated in a primary care setting. The general practitioners were free to choose the treatment and its duration. Their only obligation was to assess the therapeutic outcome in terms of efficacy and safety and to perform a final evaluation at the end of the 6-month observation period or, if the patient was treated for a shorter period, at the end of the treatment. RESULTS: Of the 476 patients involved, 308 (64.7%) responded to treatment and remained well, 117 (24.6%) showed no response, and 51 (10.7%) had an early relapse after initial improvement. Among the studied demographic, clinical, and therapeutic factors, the history of recurrent depression was the only variable with a significant effect size in predicting the course of the illness. CONCLUSION: Patients with recurrent depression were at higher risk of relapse or nonresponse.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".