Initial Effectiveness, Partial Remission, and Full Remission in Depression: Focus on Long-Term Treatment with SNRIs
Bibliographic record
Abstract
Full remission, defined as the absence of all significant symptoms of depression over at least 6 months, is the ultimate goal of antidepressant therapy. Remission takes time and studies have shown that remission rates continue to rise for at least 3 months after initial improvement. Depression is a recurrent condition with a cumulative probability of recurrence of 40% over 2 years and 70% over 5 years after the first depressive episode. In addition the risk of recurrence increases with each new depressive episode. Continuing antidepressant treatment beyond the acute response significantly decreases the risk of recurrence. A double-blind study with the serotonin norepinephrine reuptake inhibitor milnacipran, for example, has shown that patients in remission following treatment with milnacipran who continued the active treatment for a further 12 months had significantly less relapse (P<.05) than those switched to placebo. In spite of the importance of maintaining antidepressant therapy, many patients do not continue treatment. Among the principal reasons for this are side effects and worries of psychological or physical dependence. To reduce the risk of relapse, treatment with effective, well-tolerated antidepressants with few withdrawal effects should be pursued for at least 6 months and possibly longer in patients already experiencing relapse.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".