High-quality remission: potential benefits of the melatonergic approach for patients with major depressive disorder
Bibliographic record
Abstract
Full remission of symptoms is the goal for the acute treatment of depression, because incomplete remission is associated with poor outcomes including higher risk of relapse and chronicity. The current definitions for remission (e.g. a score of </=7 on the Hamilton Depression Rating Scale), however, allow for the presence of residual symptoms of depression even if remission is attained. The focus now is on the quality of remission, that is, ensuring a minimum of such residual symptoms, because the consequences of low-quality remission also include impairment in psychosocial functioning. The most common residual symptoms are sleep disturbances, fatigue, and disinterest. Sleep-associated residual symptoms are particularly common, and are a major concern because most current treatments fail to adequately address sleep disturbances and may even aggravate them. Other side effects of current treatments, such as weight gain and sexual dysfunction, may also reduce the quality of remission. A novel approach to the treatment of depression with agomelatine, a melatonergic MT1 and MT2 receptor agonist and 5-HT2C receptor antagonist, may be an effective treatment that improves the quality of remission, as it combines good efficacy with positive effects on sleep, neutral effects on sexual function, and a favorable side effect profile.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".