A new sensitive imaging biomarker for Parkinson disease?
Bibliographic record
Abstract
Over the past 25 years, considerable effort has been expended in developing neuroimaging methods capable of differentiating patients with Parkinson disease (PD) from healthy controls. These techniques have been developed with the hope that they could be used as biomarkers for both trait (presence of the disease) and state (severity of the disease), capable of convincingly establishing the diagnosis, defining the presence of the disease at its earliest stages, and serving as a surrogate marker for progression of the underlying disease. The commonest of these evaluate various components of the presynaptic dopaminergic nigrostriatal pathway.1 Others have used metabolic PET scans to identify disease-related functional brain networks.2 These techniques have relatively high sensitivity and specificity for distinguishing even patients with mild PD from healthy controls; however, there remains some overlap and they are expensive, often not widely available, and involve exposure to ionizing radiation. Over 20 years ago, MRI of the midbrain was first reported to be a promising method of demonstrating the degeneration of the substantia nigra pars compacta (SNc) in PD. Unfortunately, the original claim that narrowing of the MR signal from the SNc differentiated patients from controls on routine MRI3 was never realized. MR diffusion …
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".