Italian Version of the Parkinson Neuropsychometric Dementia Assessment (PANDA): A Useful Instrument to Detect Cognitive Impairments in Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD) is frequently characterized by cognitive and affective dysfunctions. The "Parkinson Neuropsychometric Dementia Assessment" (PANDA) is a screening tool designed for the early detection of mild cognitive impairment as well as dementia in PD. The PANDA is already validated in German and in French. OBJECTIVE: The aim of the present work was to provide normative data for the Italian-speaking population, Swiss regions included; moreover, the effectiveness of the PANDA compared to the Mini Mental State Examination (MMSE) was tested. METHODS: One-hundred and eleven PD patients with and without cognitive impairment and one-hundred and three matched healthy subjects participated at this study; all patients underwent an extensive neuropsychological evaluation. RESULTS: A PANDA total score of 13 appeared to be the most fitting cut-off with a sensitivity of 96.6% and a specificity of 82.2%; with the MMSE, the same value of sensitivity but with a specificity of 72,4% was reached only by adopting a cut-off of 28. Moreover, a PANDA range of 13-17 appeared to be suggestive for possible cognitive disturbance. CONCLUSIONS: The present work provides evidence for the effectiveness of the PANDA in evaluating cognitive deficits also in PD Italian-speaking patients, even when their pathological degree is still initial or very mild.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".