Clinical diagnostic criteria for dementia associated with Parkinson's disease
Bibliographic record
Abstract
Dementia has been increasingly more recognized to be a common feature in patients with Parkinson's disease (PD), especially in old age. Specific criteria for the clinical diagnosis of dementia associated with PD (PD-D), however, have been lacking. A Task Force, organized by the Movement Disorder Study, was charged with the development of clinical diagnostic criteria for PD-D. The Task Force members were assigned to sub-committees and performed a systematic review of the literature, based on pre-defined selection criteria, in order to identify the epidemiological, clinical, auxillary, and pathological features of PD-D. Clinical diagnostic criteria were then developed based on these findings and group consensus. The incidence of dementia in PD is increased up to six times, point-prevelance is close to 30%, older age and akinetic-rigid form are associated with higher risk. PD-D is characterized by impairment in attention, memory, executive and visuo-spatial functions, behavioral symptoms such as affective changes, hallucinations, and apathy are frequent. There are no specific ancillary investigations for the diagnosis; the main pathological correlate is Lewy body-type degeneration in cerebral cortex and limbic structures. Based on the characteristic features associated with this condition, clinical diagnostic criteria for probable and possible PD-D are proposed.
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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 | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".