Unveiling Assessment Gaps in Parkinson's Disease Psychosis: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease psychosis (PDP) is a multidimensional construct that is challenging to measure. Accurate assessment of PDP requires comprehensive and reliable clinical outcome assessment (COA) measures. OBJECTIVE: To identify PDP measurement gaps in available COAs currently used in clinical and research settings. METHODS: We conducted a scoping review using Preferred Reporting Items for Systematic Review and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines. We implemented a three-step search strategy in international databases with keywords related to Parkinson's disease (PD), psychosis, and COA. We analyzed studies using COA to assess PDP, classifying their items according to domains and subdomains. RESULTS: From 5673 identified studies, we included 628 containing 432 PDP core items from 32 COAs. Among the 32 COAs, 19 were PD-specific, containing 266 items, constructed as clinician-reported outcomes (ClinRO) (148 items), patient-reported outcomes (PRO) (112 items), and observer-reported outcomes (ObsRO) (six items). Across all PD-specific COAs, regardless of structure, 89.4% of the items from 27 COAs focused primarily on assessing PDP symptoms' severity, and only 9.7% of items probed the impact of PDP on a person's daily functioning. CONCLUSIONS: Symptom-based domains are currently prioritized for measuring the severity of PDP, with limited coverage of the functional impact of PDP on patients' lives. Whereas the International Parkinson and Movement Disorder Society has traditionally developed a "Unified" COA that culls items from prior COAs to form a new one, a new COA will largely need newly developed items if the functional impact of PDP is prioritized. © 2024 International Parkinson and Movement Disorder Society.
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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 | 0.070 | 0.256 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.035 | 0.024 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| 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".