Elderly Patients With Dementia-Related Symptoms of Severe Agitation and Aggression
Bibliographic record
Abstract
OBJECTIVE: Atypical antipsychotic drugs have been used off label in clinical practice for treatment of serious dementia-associated agitation and aggression. Following reports of cerebrovascular adverse events associated with the use of atypical antipsychotics in elderly patients with dementia, the U.S. Food and Drug Administration (FDA) issued black box warnings for several atypical antipsychotics titled "Cerebrovascular Adverse Events, Including Stroke, in Elderly Patients With Dementia." Subsequently, the FDA initiated a metaanalysis of safety data from 17 registration trials across 6 antipsychotic drugs (5 atypical antipsychotics and haloperidol). In 2005, the FDA issued a black box warning regarding increased risk of mortality associated with the use of atypical antipsychotic drugs in this patient population. PARTICIPANTS: Geriatric mental health experts participating in a 2006 consensus conference (Bethesda, Md., June 28-29) reviewed evidence on the safety and efficacy of antipsychotics, as well as nonpharmacologic approaches, in treating dementia-related symptoms of agitation and aggression. EVIDENCE/CONSENSUS PROCESS: The participants concluded that, while problems in clinical trial designs may have been one of the contributors to the failure to find a signal of drug efficacy, the findings related to drug safety should be taken seriously by clinicians in assessing the potential risks and benefits of treatment in a frail population, and in advising families about treatment. Information provided to patients and family members should be documented in the patient's chart. Drugs should be used only when nonpharmacologic approaches have failed to adequately control behavioral disruption. Participants also agreed that there is a need for an FDA-approved medication for the treatment of severe, persistent, or recurrent dementia-related symptoms of agitation and aggression (even in the absence of psychosis) that are unresponsive to nonpharmacologic intervention. CONCLUSIONS: This article outlines methodological enhancements to better evaluate treatment approaches in future registration trials and provides an algorithm for improving the treatment of these patients in nursing home and non-nursing home settings.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".