Clinical prediction of Parkinson's disease: planning for the age of neuroprotection
Bibliographic record
Abstract
As a chronic progressive disease, Parkinson's disease (PD) has a presymptomatic interval; that is, a period during which the pathological process has begun, but motor signs required for the clinical diagnosis are absent. The ability to identify this preclinical stage may be critical in the development and eventual use of neuroprotective therapy. Recently proposed staging systems of PD have suggested that degeneration may occur initially in areas outside the substantia nigra, suggesting that non-motor manifestations may be markers of presymptomatic PD. Decreased olfaction has recently been demonstrated to predict PD in prospective pathological studies, although the lead time may be relatively short, and the positive predictive value is low. Idiopathic RBD has a very high predictive value, with approximately 50% of affected individuals developing PD or dementia within 10 years. This implies that idiopathic RBD patients are ideal candidates to test potential preclinical markers. However, the specificity of symptom screens for RBD is not established, not all persons with PD develop RBD, and there are only limited ways to predict which RBD patients will develop PD. Other simple screens based upon autonomic symptoms, depression and personality changes, quantitative motor testing and other sleep disorders may also be useful markers, but have not been extensively tested. Other more expensive measures such as detailed autonomic testing, cardiac MIBG-scintigraphy, dopaminergic imaging and transcranial ultrasound may be especially useful in defining disease risk in those identified through primary screening.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".