Pharmacotherapy to sustain the fully remitted state
Bibliographic record
Abstract
Full remission should be the goal of antidepressant therapy; anything less leaves the patient with residual symptoms and an increased risk of relapse and recurrence. Most antidepressant agents offer similar rates of response, but there are some differences in the ability of different agents to promote a full remission. The greatest chance of achieving full remission occurs early in the course of treatment; thus, initial antidepressant strategies should be those that have the greatest therapeutic potential. Other strategies that may help improve the chances of achieving full remission include optimizing drug dosages and using combination and augmentation strategies. Failure to achieve full remission and early discontinuation of antidepressant therapy have been associated with a greater incidence of relapse and recurrence. Continued antidepressant therapy has clearly been shown to effectively reduce the probability of relapse and recurrence by about half compared with placebo. Therefore, once a patient achieves remission, it is important to continue the same antidepressant therapy for at least 6-12 months and, for many patients, considerably longer. Medication should continue at the dose that was initially effective because using low-dose maintenance therapy appears to decrease the protective benefits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".