Biomarkers for trials of neuroprotection in Parkinson's disease
Bibliographic record
Abstract
With increased understanding of disease pathogenesis and the foreseeable reality of disease-modifying therapies, there is a growing need to find biomarkers that will allow early (preferably preclinical) detection of disease and that will provide an independent readout of disease progression. In this article, we review a variety of markers, with a focus on functional imaging techniques, which while imperfect, currently provide the best approach to this problem. We consider the limitations of functional imaging of the dopamine system in assessing the progression of Parkinson's Disease (PD) as well as the potential use of structural imaging and emerging progress in other biochemical and molecular markers. While there is no single biomarker that will satisfy all requirements, some combination is likely to be of great use in identifying those subjects most likely to benefit from neuroprotective therapies, as well as in monitoring the effects of these interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".