The role of biomarkers and imaging in Parkinson’s disease
Bibliographic record
Abstract
The diagnosis of Parkinson's disease (PD) currently relies on the appearance of certain clinical features. However, these features appear only years after the loss of nigral dopaminergic neurons. The progression of PD may be measured using clinical rating scales that are subjective and that have a variable inter-rater consistency. There is a growing need for a biomarker that will allow for early detection of the disease as well as provide a measure of disease progression. In this article, we review different biomarkers, with a focus on functional imaging techniques, which while imperfect, currently provide the best approach to this problem. We also discuss the use of structural imaging and emerging progress in other biochemical and molecular markers. While there is no single biomarker that will satisfy all requirements, a 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 any 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 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".