Parkinson's Disease‐Cognitive Rating Scale (<scp>PD‐CRS</scp>): Normative Data and Mild Cognitive Impairment Assessment in Brazil
Bibliographic record
Abstract
Background: The Parkinson's Disease-Cognitive Rating Scale (PD-CRS) assesses posterior-cortical and frontal-subcortical cognitive functioning and distinguishes mild cognitive impairment in Parkinson's disease (PD-MCI); however, it was not evaluated in Brazil. Objectives: To investigate PD-CRS's reliability, validity, normative data, and accuracy for PD-MCI screening in Brazil. Methods: The effects of age, education, and sex on PD-CRS scores were explored. The instrument was tested in 714 individuals (53% female, 21-94 years), with a broad range of education and no neurodegenerative disorder. Trail Making, Consonant Trigrams, Five-Point, and semantic fluency tests were administered for comparison. A second study enrolled patients with PD and intact cognition (n = 44, 59.75 ± 10.79 years) and with PD-MCI (n = 25, 65.76 ± 10.33 years) to investigate criterion validity. PD-CRS subtests were compared with the Cambridge Automated Neuropsychological Battery memory and executive tasks. Results: PD-CRS was unidimensional and reliable (McDonald's ω = 0.83). Using robust multiple regressions, age, and education predicted the total and derived scores in the normative sample. At the 85-point cutoff, PD-MCI was detected with 68% sensitivity and 86% specificity (area under the curve = 0.870). PD-CRS scores strongly correlated with executive and verbal/visual memory tests in both normative and clinical samples. Conclusions: This study investigated the applicability of PD-CRS in the Brazilian context. The scale seems helpful in screening for PD-MCI, with adequate internal consistency and construct validity. The PD-CRS variance is influenced by age and educational level, a critical issue for cognitive testing in countries with educational and cultural heterogeneity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".