Practice Parameter: Evaluation and treatment of depression, psychosis, and dementia in Parkinson disease (an evidence-based review): [RETIRED]
Bibliographic record
Abstract
OBJECTIVE: To make evidence-based treatment recommendations for patients with Parkinson disease (PD) with dementia, depression, and psychosis based on these questions: 1) What tools are effective to screen for depression, psychosis, and dementia in PD? 2) What are effective treatments for depression and psychosis in PD? 3) What are effective treatments for PD dementia or dementia with Lewy bodies (DLB)? METHODS: A nine-member multispecialty committee evaluated available evidence from a structured literature review using MEDLINE, and the Cochrane Database of Health and Psychosocial Instruments from 1966 to 2004. Additional articles were identified by panel members. RESULTS: The Beck Depression Inventory-I, Hamilton Depression Rating Scale, and Montgomery Asberg Depression Rating Scale should be considered to screen for depression in PD (Level B). The Mini-Mental State Examination and the Cambridge Cognitive Examination should be considered to screen for dementia in PD (Level B). Amitriptyline may be considered to treat depression in PD without dementia (Level C). For psychosis in PD, clozapine should be considered (Level B), quetiapine may be considered (Level C), but olanzapine should not be considered (Level B). Donepezil or rivastigmine should be considered for dementia in PD (Level B) and rivastigmine should be considered for DLB (Level B). CONCLUSIONS: Screening tools are available for depression and dementia in patients with PD, but more specific validated tools are needed. There are no widely used, validated tools for psychosis screening in Parkinson disease (PD). Clozapine successfully treats psychosis in PD. Cholinesterase inhibitors are effective treatments for dementia in PD, but improvement is modest and motor side effects may occur.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".