Predict cognitive decline with clinical markers in Parkinson’s disease (PRECODE-1)
Bibliographic record
Abstract
Over the course of the disease, about 80% of Parkinson's disease patients will develop cognitive impairment. However, predictive factors associated with cognitive decline are still under investigation. Here, we investigated which clinically available markers are predictive of cognitive impairment in a cohort of early drug-naïve Parkinson's disease patients. 294 drug-naïve Parkinson's disease patients, who were cognitively normal at baseline, were recruited from the Parkinson's Progression Markers Initiative. At 36-month follow-up, patients were diagnosed with cognitive impairment according to two levels: Level 1 diagnosis was defined as MoCA < 26 and Level 2 diagnosis was defined as MoCA < 26, alongside an impaired score on at least two neuropsychological tests. Predictive variables with a validated cut-off were divided into normal or abnormal measures, whilst others were divided into normal or abnormal measures based on the decile with the highest power of prediction. At 3 years' follow-up, 122/294 Parkinson's disease (41.5%) patients had cognitive decline. We found that age at Parkinson's disease onset, MDS-UPDRS Part-III, Hopkin's Learning Verbal Test-Revised Recall, Semantic Fluency Test and Symbol Digit Modalities Test were all predictors of cognitive decline. Specifically, age at Parkinson's disease onset, Semantic Fluency Test and symbol Digit Modalities Test were predictors of cognitive decline defined by Level 2. The combination of three abnormal tests, identified as the most significant predictors of cognitive decline, gave a 63.6-86.7% risk of developing cognitive impairment defined by Level 2 and Level 1 criteria, respectively, at 36-month follow-up. Our findings show that these clinically available measures encompass the ability to identify drug-naïve Parkinson's disease patients with the highest risk of developing cognitive impairment at the earliest stages. Therefore, by implementing this in a clinical setting, we can better monitor and manage patients who are at risk of cognitive decline.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".