Update of the MDS research criteria for prodromal Parkinson's disease
Bibliographic record
Abstract
The MDS Research Criteria for Prodromal PD allow the diagnosis of prodromal Parkinson's disease using an evidence-based conceptual framework, which was designed to be updated as new evidence becomes available. New prospective evidence of predictive values of risk and prodromal markers published since 2015 was reviewed and integrated into the criteria. Many of the predictive values (likelihood ratios, LR) remain unchanged. The positive likelihood ratio notably increase for olfactory loss and decreased for substantia nigra hyperechogenicity. Negative likelihood ratio remained largely unchanged for all markers. New levels of diagnostic certainty for neurogenic and symptomatic orthostatic hypotension have been added, which substantially differ in positive likelihood ratio from the original publication. For intermediate strength genetic variants, their age-related penetrance is now incorporated in the calculation of the positive likelihood ratio. Moreover, apart from prospective studies, evidence from cross-sectional case-control genome-wide association studies is also considered (given their likely lack of confounding and reverse causation), and to account for the effect of multiple low-penetrance genetic variants polygenic risk scores are added to the model. Diabetes, global cognitive deficit, physical inactivity, and low plasma urate levels in men enter the criteria as new markers. A web-based prodromal PD risk calculator allows the calculation of probabilities of prodromal PD for individuals. Several promising candidate markers may improve the diagnostic accuracy of prodromal PD in the future. © 2019 International Parkinson and Movement Disorder Society.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".