Reader Response: Cognitive-Driven Activities of Daily Living Impairment as a Predictor for Dementia in Parkinson Disease: A Longitudinal Cohort Study
Bibliographic record
Abstract
The article by Becker et al.1 presents highly relevant longitudinal data and poses the question of how to monitor cognition and select participants for clinical trials. For this, it suggests the comprehensive Functional Activities Questionnaire (FAQ) as a tool.2 In an attempt to explore whether cognition can be assessed longitudinally with less time-consuming bedside tests, we evaluated existing data from the EPIPARK study, which offers longitudinal data of 655 cases with Parkinson disease and 622 controls.3 Specifically, we calculated the correlation between the frequently used Montreal Cognitive Assessment (MoCA) and the Unified Parkinson Disease Rating Scale (UPDRS) question on cognition (r = −0.319, 95% CI −0.368 to −0.268).4 This moderate correlation is not sufficient to replace the MoCA with this single UPDRS question. However, the MoCA sum score itself correlates across time points 1 and 2 after a year (r = 0.705 [95% CI 0.649–0.754]) and time point 1 to present (12 years of follow-up; r = 0.522 [95% CI 0.411–0.618]). In light of this data, it would be interesting to learn whether the MoCA and the cognition section of the FAQ correlate in the study conducted by Becker et al. and whether the 2 could be used interchangeably to facilitate broad screening of patients for the development of Parkinson disease dementia.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.018 | 0.009 |
| Insufficient payload (model declined to judge) | 0.023 | 0.021 |
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".