Subtle motor disturbances in PREDICT-PD participants
Bibliographic record
Abstract
OBJECTIVE: The PREDICT-PD study aims to identify increased risk of Parkinson''s disease (PD) using online assessments of previously identified risk and early features of PD and an evidence-based scoring algorithm. We sought to determine whether higher risk participants (defined as those above the 15th centile of risk estimates) were more likely to have mild parkinsonian signs compared with lower risk participants. METHODS: Video recordings of neurological examinations, including the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III, of 208 individuals who had previously completed an online risk assessment were scored blindly and independently by two movement-disorders experts. Higher risk and lower risk subjects were compared for MDS-UPDRS part III score (and derivations of this) to identify subclinical parkinsonism, and association of risk estimates with MDS-UPDRS III scores assessed. RESULTS: Higher risk subjects had significantly higher median UPDRS part III scores (3, IQR 1-5.5) than lower risk subjects (1, IQR 0-3.0; p<0.001), and there was a significantly greater proportion of individuals classified as having subclinical parkinsonism. 18% of the higher risk subjects and 6% of the lower risk subjects exceeded the most stringent published cut-off for subtle parkinsonism of three definitions examined (p=0.027). Linear regression analysis demonstrated a continuous relationship of log-transformed risk estimates with UPDRS part III scores (increase in MDS-UPDRS per doubling of odds 0.52, 95% CI 0.31 to 0.72; p<0.001), which remained after adjustment for multiple vascular risk factors and scores on the Montreal Cognitive Assessment (0.58, 95% CI 0.30 to 0.87; p<0.001). CONCLUSIONS: The PREDICT-PD algorithm identifies a population with an increased rate of motor disturbances.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".