The Effect of Initial Duloxetine Dosing Strategy on Nausea in Korean Patients with Major Depressive Disorder
Bibliographic record
Abstract
OBJECTIVE: To assess the relative severity of nausea in patients from Korea with major depressive disorder (MDD) who were treated with duloxetine at low (30 mg) or high (60 mg) doses, with or without food, for the first week of an 8 week treatment. METHODS: Adult patients (n=249), with MDD and a 17-item Hamilton Rating Scale for Depression (HAMD(17)) score of ≥15, received open-label once daily duloxetine. At Week 0, patients were randomized to 4 groups: 30 mg with food (n=63), 60 mg with food (n=59), 30 mg without food (n=64), and 60 mg without food (n=63). At Week 1, all patients switched to duloxetine 60 mg for 7 weeks. The primary outcome measure was item 112 (nausea) of the Association for Methodology and Documentation in Psychiatry adverse event scale. Effectiveness was assessed by change in HAMD(17) total score. RESULTS: Overall, 94.4% (235/249) of patients completed Week 1 and 55.0% (137/249) of patients completed the study. For Week 1, nausea was significantly less severe for patients who received 30 mg compared with 60 mg duloxetine (p=0.003), regardless of food intake. In all groups, nausea severity was highest at Week 1 and declined throughout the study. HAMD(17) score was reduced in all groups and the most common adverse event reported was nausea (145/249; 58.2%). CONCLUSION: To minimize nausea, Korean patients with MDD who require duloxetine treatment could be given 30 mg once daily, regardless of food, for the first week followed by 60 mg once daily for the course of therapy.
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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 | 0.000 | 0.000 |
| 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".