Premotor Parkinson's disease: Concepts and definitions
Bibliographic record
Abstract
Parkinson's disease (PD) has a prodromal phase during which nonmotor clinical features as well as physiological abnormalities may be present. These premotor markers could be used to screen for PD before motor abnormalities are present. The technology to identify PD before it reaches symptomatic Braak Stage 3 (substantia nigra compacta [SNc] involvement) already exists. The current challenge is to define the appropriate scope of use of predictive testing for PD. Imaging technologies such as dopamine transporter imaging currently offer the highest degree of accuracy for identifying premotor PD, but they are expensive as screening tools, and abnormalities on these studies would only be evident at Braak Stage 3 or higher. Efficiency is greatly enhanced by combining imaging with a prescreening test such as olfactory testing. This 2-step process has the potential to greatly reduce costs while retaining diagnostic accuracy. Alternatively, or in concert with this approach, evaluating high-risk populations (eg, patients with rapid eye movement behavior disorder or LRRK2 mutations) would enrich the sample for cases with underlying PD. Ultimately, the role of preclinical detection of PD will be determined by the ability of emerging therapies to influence clinical outcomes. As such, implementation of large-scale screening strategies awaits the arrival of clearly safe and effective therapies that address the underlying pathogenesis of PD. Future research will establish more definitive biomarkers capable of revealing the presence of disease in advance of SNc involvement with the promise of the potential for introducing disease-modifying therapy even before the development of evidence of dopamine deficiency.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".