Psychotropic and Cognitive-Enhancing Medication Use and Its Documentation in Contemporary Long-term Care Practice
Bibliographic record
Abstract
BACKGROUND: In long-term care (LTC) settings, use of psychotropic medications to manage behavioral and psychological symptoms of dementia and use of cognitive enhancers are commonplace. It is important that these medications are properly used to ensure resident well-being, and thus, it is paramount to understand use of these medications in contemporary practice to develop appropriate quality improvement initiatives. OBJECTIVE: To characterize psychotropic and cognition-enhancing medication use LTC residents and current trends in documentation. METHODS: Cross-sectional chart review of residents aged >65 years with dementia receiving psychotropic medications and/or cognitive enhancers. RESULTS: From 180 residents, 84 (82% female) met inclusion criteria (average age 86 years). The prevalence of psychotropic medication use was as follows: cognitive enhancers, 71%; antidepressants, 98%; antipsychotics, 61%; sedative hypnotics, 23%. Quetiapine was the most commonly used antipsychotic (48%), followed by risperidone (28%) and olanzapine (15%), all of which were dosed within accepted guidelines. The duration of therapy ranged from 2 to 5 years for antipsychotic medications and 1¼ to 3 years for antidepressants. Documentation documentation rates were hightest for psychotropics versus cognitive enhancers. There was no documentation of attempts to lower doses or discontinue psychotropic medications or cognitive enhancers. CONCLUSIONS: Many, but not all psychotropics used were acceptable choices. The duration of therapy appears to be excessive for antipsychotic medications. Documentation of ongoing need for medications varied and could be improved on to better assess residents' medication regimens. Further research will inform efforts to enhance the care of these residents.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".