Prodromal Parkinson's Disease: The Decade Past, the Decade to Come
Bibliographic record
Abstract
The past decade has seen a dramatic expansion of the field of prodromal PD. Ten years ago, there were only six known prodromal markers of disease, none of which had more than two studies documenting diagnostic value. We now have at least 16 markers, with as many as 10 prospective studies for a single marker. This review summarizes the major advances over the last decade and speculates about the advances we will see in the decade to come. The most notable advances over the last decade came through the study of high-risk cohorts (REM sleep behavior disorder and later genetic and autonomic cohorts), the generation of more representative population-based cohorts for studying prodromal PD, major advances in neuroimaging of early disease stages, the emerging likelihood that tissue biopsy will be able to diagnose prodromal PD, and the coalescence of prodromal markers into discrete criteria. As the next decade dawns, we await increasing precision of sensitivity and specificity estimates of known markers, the discovery of new biomarkers of prodromal disease, improvements in diagnosis using combined methods/criteria (with increasing recognition of prodromal PD as one stage of the full PD spectrum), and ultimately the development of neuroprotective therapy that can be provided at the earliest stages of disease. © 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".