Progress in Pharmacologic Management of Neuropsychiatric Syndromes in Neurodegenerative Disorders
Bibliographic record
Abstract
Importance: Neuropsychiatric syndromes (NPSs) are common in neurodegenerative disorders (NDDs); compromise the quality of life of patients and their care partners; and are associated with faster disease progression, earlier need for nursing home care, and poorer quality of life. Advances in translational pharmacology, clinical trial design and conduct, and understanding of the pathobiology of NDDs are bringing new therapies to clinical care. Observations: Consensus definitions have evolved for psychosis, agitation, apathy, depression, and disinhibition in NDDs. Psychosocial interventions may reduce mild behavioral symptoms in patients with NDD, and pharmacotherapy is available for NPSs in NDDs. Brexpiprazole is approved for treatment of agitation associated with Alzheimer disease dementia, and pimavanserin is approved for treatment of delusions and hallucinations associated with psychosis of Parkinson disease. Trials are being conducted across several of the NDDs, and a variety of mechanisms of action are being assessed for their effect on NPSs. Conclusions and Relevance: Detection and characterization of NPSs in patients with NDDs is the foundation for excellent care. New definitions for NPSs in NDDs may inform choices regarding clinical trial populations and translate into clinical practice. Psychosocial and pharmacologic therapies may reduce behavioral symptoms and improve quality of life for patients and caregivers. Approved agents may establish regulatory precedents, demonstrate successful trial strategies, and provide the foundation for further advances in treatment development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".