Predictors of cognitive impairment in an early stage Parkinson's disease cohort
Bibliographic record
Abstract
The impact of Parkinson's disease (PD) dementia is substantial and has major functional and socioeconomic consequences. Early prediction of future cognitive impairment would help target future interventions. The Montreal Cognitive Assessment (MoCA), the Mini-Mental State Examination (MMSE), and fluency tests were administered to 486 patients with PD within 3.5 years of diagnosis, and the results were compared with those from 141 controls correcting for age, sex, and educational years. Eighteen-month longitudinal assessments were performed in 155 patients with PD. The proportion of patients classified with normal cognition, mild cognitive impairment (MCI), and dementia varied considerably, depending on the MoCA and MMSE thresholds used. With the MoCA total score at screening threshold, 47.7%, 40.5%, and 11.7% of patients with PD were classified with normal cognition, MCI, and dementia, respectively; by comparison, 78.7% and 21.3% of controls had normal cognition and MCI, respectively. Cognitive impairment was predicted by lower education, increased age, male sex, and quantitative motor and non-motor (smell, depression, and anxiety) measures. Longitudinal data from 155 patients with PD over 18 months showed significant reductions in MoCA scores, but not in MMSE scores, with 21.3% of patients moving from normal cognition to MCI and 4.5% moving from MCI to dementia, although 13.5% moved from MCI to normal; however, none of the patients with dementia changed their classification. The MoCA may be more sensitive than the MMSE in detecting early baseline and longitudinal cognitive impairment in PD, because it identified 25.8% of those who experienced significant cognitive decline over 18 months. Cognitive decline was associated with worse motor and non-motor features, suggesting that this reflects a faster progressive phenotype.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".