Longitudinal progression of sporadic Parkinson's disease: a multi-tracer positron emission tomography study
Bibliographic record
Abstract
Parkinson's disease is a heterogeneous disorder with multiple factors contributing to disease initiation and progression. Using serial, multi-tracer positron emission tomography imaging, we studied a cohort of 78 subjects with sporadic Parkinson's disease to understand the disease course better. Subjects were scanned with radiotracers of presynaptic dopaminergic integrity at baseline and again after 4 and 8 years of follow-up. Non-linear multivariate regression analyses, using random effects, of the form BP(ND)(t) or K(occ)(t) = a*e((-)(bt)(-d)(A) + c, where BP(ND) = tracer binding potential (nondispaceable), K(OCC) = tracer uptake constant a, b, c and d are regression parameters, t is the symptom duration and A is the age at onset, were utilized to model the longitudinal progression of radiotracer binding/uptake. We found that the initial tracer binding/uptake was significantly different in anterior versus posterior striatal subregions, indicating that the degree of denervation at disease onset was different between regions. However, the relative rate of decline in tracer binding/uptake was similar between the striatal subregions. While an antero-posterior gradient of severity was maintained for dopamine synthesis, storage and reuptake, the asymmetry between the more and less affected striatum became less prominent over the disease course. Our study suggests that the mechanisms underlying Parkinson's disease initiation and progression are probably different. Whereas factors responsible for disease initiation affect striatal subregions differently, those factors contributing to disease progression affect all striatal subregions to a similar degree and may therefore reflect non-specific mechanisms such as oxidative stress, inflammation or excitotoxicity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".