Diagnostic procedures for Parkinson's disease dementia: Recommendations from the movement disorder society task force
Bibliographic record
Abstract
A preceding article described the clinical features of Parkinson's disease dementia (PD-D) and proposed clinical diagnostic criteria for "probable" and "possible" PD-D. The main focus of this article is to operationalize the diagnosis of PD-D and to propose practical guidelines based on a two level process depending upon the clinical scenario and the expertise of the evaluator involved in the assessment. Level I is aimed primarily at the clinician with no particular expertise in neuropsychological methods, but who requires a simple, pragmatic set of tests that are not excessively time-consuming. Level I can be used alone or in concert with Level II, which is more suitable when there is the need to specify the pattern and the severity on the dementia of PD-D for clinical monitoring, research studies or pharmacological trials. Level II tests can also be proposed when the diagnosis of PD-D remains uncertain or equivocal at the end of a Level I evaluation. Given the lack of evidence-based standards for some tests when applied in this clinical context, we have tried to make practical and unambiguous recommendations, based upon the available literature and the collective experience of the Task Force. We accept, however, that further validation of certain tests and modifications in the recommended cut off values will be required through future studies.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".