10.1708/1178.13054
Bibliographic record
Abstract
INTRODUCTION: The elderly population is more frequently subjected to depressive mood compared to the general population and show peculiarities affecting responsiveness; furthermore, aged people need also special care. Duloxetine is a relatively new antidepressant that proved to be effective in adult depression, but has received little attention in elderly population heretofore. AIM: To review the evidence of duloxetine in late-life major depressive disorder (MDD). METHOD: A systematic review of studies focusing on the use of duloxetine in MDD in the elderly has been carried out through the principal specialized databases, including PubMed, PsycLIT, and Embase. RESULTS: Only a handful of papers were specifically dedicated to this issue. Duloxetine was found to be effective and safe in old-age MDD, to be better than placebo on many clinical measures in all studies, and to better differentiate from placebo with respect to selective serotonin reuptake inhibitors. Compared to placebo, its side-effect profile is slightly unfavorable and its drop-out rate is slightly higher. Furthermore, when pain is present in old-age MDD, duloxetine is able to reduce it. CONCLUSIONS: The efficacy and safety of duloxetine in old-age depression are similar to those encountered in adult MDD. There is a relative lack of comparative studies other than with placebo. The special needs of elderly patients with MDD must be addressed with close patient contact to avoid the perils of inappropriate dosing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.684 | 0.658 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".