Treatment options for sundowning in patients with dementia
Bibliographic record
Abstract
Objective. To review the evidence for the pharmacologic and non-pharmacologic management of sundowning in patients with dementia. Methods. Databases were searched using the terms sundown, circadian, chronobiological, biological clock, elderly, aged, geriatric, and senior. Studies selected for inclusion assessed potential interventions for the treatment of sundowning or nocturnal agitation. Results. A total of thirteen individual studies and two systematic reviews were evaluated. Study design and outcomes varied, but many measured sleep and nocturnal agitation. Non-pharmacologic interventions that may be of benefit include bright light therapy, music therapy, and aromatherapy. Pharmacologic therapies generally provided minimal benefit and were associated with safety concerns. Supportive evidence was found for the use of melatonin and antipsychotics. Evidence for antidepressants, donepezil, and dronabinol was weaker. Supportive evidence for the use of benzodiazepines was not found and thus cannot be recommended in elderly patients as they are more susceptible to their adverse effects. Conclusion. The number of studies on the management of sundowning is limited and the quality of evidence supporting its treatment is weak. Non-pharmacologic interventions are first line due to safety. Pharmacologic agents are recommended as second line treatment options, in particular antipsychotics and melatonin.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".