The safety and tolerability of duloxetine in depressed elderly patients with and without medical comorbidity
Bibliographic record
Abstract
AIM AND METHODS: The impact of medical comorbidity on the efficacy and tolerability of duloxetine in elderly patients with major depressive disorder (MDD) was investigated in this study. Data were obtained from a multicentre, randomised, double-blind, placebo-controlled study in 311 patients with MDD aged 65-89. The primary outcome measure was a prespecified composite cognitive score based on four cognitive tests: (i) Verbal Learning and Recall Test; (ii) Symbol Digit Substitution Test; (iii) 2-Digit Cancellation Test and (iv) Letter-Number Sequencing Test. Secondary measures included the Geriatric Depression Scale (GDS), 17-Item Hamilton Depression Scale (HAMD17), Clinical Global Impression-Severity (CGI-S) Scale, Visual Analogue Scale (VAS) for pain and 36-Item Short Form Health Survey (SF-36). Tolerability measures included adverse events reported as the reason for discontinuation and treatment-emergent adverse events (TEAEs). The consistency of the effect of duloxetine vs. placebo comparing patients with and without medical comorbidity (vascular disease, diabetes, arthritis or any of these) was investigated. RESULTS: Overall, duloxetine 60 mg/day demonstrated significantly greater improvement compared with placebo for the composite cognitive score, GDS and HAMD17 total scores, CGI-Severity, HAMD17 response and remission rates, and some of the SF-36 and VAS measures. There were few significant treatment-by-comorbidity subgroup interactions for these efficacy variables, or for adverse events reported as the reason for discontinuation and common TEAEs. CONCLUSIONS: The present analyses suggested that the efficacy of duloxetine on cognition and depression in elderly patients, and its tolerability, were not largely affected by the comorbidity status. These results further support the use of duloxetine in elderly patients with MDD.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".