An update on antidepressant pharmacotherapy in late-life depression
Bibliographic record
Abstract
Introduction: Clinically important depressive symptoms that occur in adults over age 60 are often termed late-life depression (LLD). LLD poses challenges for treating clinicians in both detection and treatment. Antidepressants are the most common first-line treatment approach. Older adults are at an increased risk of adverse effects because of polypharmacy.Areas covered: This article summarizes the challenges and approaches when using pharmacotherapy in LLD with a focus on newer data that have become available during the last five years. While no new antidepressants have become available during this period, a review of the literature summarizes advances in the knowledge of the adverse effects associated with various antidepressants and on the potential contribution of pharmacogenetic tools when prescribing antidepressants to older patients.Expert opinion: During the past 5 years, most of the literature relevant to the pharmacotherapy of MDD in older patients has focused on adverse effects. In particular, the effects of antidepressants on cognition and bone are emerging as important areas for clinical attention and further investigation. There is also an emerging literature on the potential role of pharmacogenetic testing in patients with MDD, though recommendations for use in older adults await larger studies that demonstrate its efficacy and cost-effectiveness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".