Early Improvements in Individual Symptoms to Predict Later Remission in Major Depressive Disorder Treated With Mirtazapine
Bibliographic record
Abstract
Few studies, to our knowledge, have examined whether early improvements in individual, instead of overall, depressive symptoms predict remission in major depressive disorder (MDD). This post hoc analysis used data from 194 patients with MDD enrolled in a 6-week double-blind, placebo-controlled, randomized trial of mirtazapine, to identify improvements in specific individual depressive symptoms in the early phase that are associated with subsequent remission. Trajectories of individual depressive symptoms over 6 weeks were compared between remitters and nonremitters. Early improvement was defined as a ≥20% decrease in the Hamilton Rating Scale for Depression 17 items (HAM-D17) total score in weeks 1 and 2, and remission was defined as a HAM-D17 final score of ≤7. Reliability parameters were calculated for early improvements in predicting later remission. Whether improvement in each of the HAM-D17 symptoms in weeks 1 or 2 predicted remission was examined, using binary logistic regression analyses. As a result, improvements in weeks 1 and 2 were associated with sensitivity of 0.82 and 0.99 and specificity of 0.54 and 0.44, respectively, in predicting remission in week 6. Improvements in insomnia late (P = .04) and insight (P = .007) in week 1 and somatic symptoms general (P = .002) and insight (P = .04) in week 2 were associated with remission in week 6. In conclusion, early improvements in insight, insomnia late, and somatic symptoms general, as well as overall depressive symptoms, may serve as specific clinical indicators of subsequent remission in patients with MDD receiving mirtazapine.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".