Impact of psychotropic medications on cognition among older adults: a systematic review
Bibliographic record
Abstract
OBJECTIVES: The aim of this systematic review is to examine the cognitive impact of psychotropic medications including benzodiazepines, antidepressants, mood stabilizers, antipsychotics, or a combination of these drugs on older adults. DESIGN: Systematic review. SETTING: We searched Medline, PsycINFO, and Embase through the Ovid platform, CINAHL through EBSCO, and Web of Science. PARTICIPANTS AND INTERVENTIONS: Randomized control trials (RCTs) and cohort studies that used a validated scale to measure cognition with a follow-up period of at least six months were included. MEASUREMENT: The primary outcome of interest was cognitive change associated with psychotropic medication use. RESULTS: A total of 7551 articles were identified from the primary electronic literature search across the five databases after eliminating duplicates. Based on full-text analysis, 27 articles (two RCTs, 25 cohorts) met the inclusion criteria. Of these, nine each examined the impact of benzodiazepines and antidepressants, five examined psychotropic combinations, three on antipsychotic drugs, and one on the effects of mood stabilizers. CONCLUSIONS: This is the first systematic review to examine the cognitive impact of multiple psychotropic drug classes in older adults over an extended follow-up period (six months or more) using robust sample sizes, drug-free control groups, and validated cognitive instruments. We found evidence to indicate cognitive decline with the cumulative use of benzodiazepines and the use of antidepressants, especially those with anticholinergic properties among older adults without cognitive impairment at baseline. Further, the use of antipsychotics and psychotropic combinations is also associated with cognitive decline in older adults.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".