Clinical Trials in Parkinson's Disease Dementia and Dementia with Lewy Bodies
Bibliographic record
Abstract
Parkinson's disease with dementia (PDD) and dementia with Lewy bodies (DLB) are pathological overlapping and important causes of dementia for which clinical trials are in their infancy. Cholinesterase inhibitors may be of benefit in DLB and PDD, as suggested by placebo-controlled clinical trials of rivastigmine and donepezil. The anti-psychotic agent clozapine has been of benefit in PD and PDD, but other agents, such as quetiapine, require adequate assessment. Barriers to trials include pathological overlap that can lead to inaccuracies in clinical diagnosis, unavailability of a consensus definition for PDD, unanswered questions regarding natural history and the paucity of validated outcome measures. Motor impairment must be considered in patients with PDD and DLB; conversely, cognitive impairment should be assessed in trials targeting motor impairment in advanced PD. Potential targets for treatment include onset of dementia, cognitive impairment, behavioral impairment, functional decline, falls, nursing home placement, mortality, quality of life and economic impact. Biomarkers including neuroimaging and cerebrospinal fluid markers are not currently established. At present PDD and DLB are distinct entities by definition. Future studies, including clinical trials and biomarker studies, will help to further define the clinical and therapeutic implications of this distinction.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".