Predicting cognitive decline with non-clinical markers in Parkinson’s disease (PRECODE-2)
Bibliographic record
Abstract
OBJECTIVES: I]FP-CIT SPECT and CSF markers can predict cognitive impairment (CI) in PD patients, and provide a profile of those most at risk. METHODS: 262 de novo PD patients from the Parkinson's Progression Markers Initiative database were stratified into two CI groups at the 36-month follow-up: MoCA-defined diagnosis: PD patients who had a MoCA score < 26; neuropsychological test-defined diagnosis: PD patients with MoCA-defined diagnosis and at least two test scores (of six; irrespective of test domain) greater than 1.5 standard deviation below the mean score in healthy controls. Predictive variables of CI were divided into deciles, providing us with ideal cutoff values for each variable. RESULTS: I]FP-CIT SPECT uptake (< 1.82) had a 65% risk of developing CI at 36-month follow-up. CONCLUSION: I]FP-CIT SPECT uptake) that enables identification of early PD patients at risk of developing CI. These findings confirm previous reports of low CSF Aβ42, elevated CSF total tau and reduced dopaminergic integrity being associated with cognitive decline in PD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".