Changes of Dopamine Turnover in the Progression of Parkinson's Disease as Measured by Positron Emission Tomography: Their Relation to Disease-Compensatory Mechanisms
Bibliographic record
Abstract
An increase in dopamine turnover has been shown to occur early in Parkinson's disease (PD). This study investigated changes of dopamine turnover as a function of PD duration using the effective distribution volume (EDV) for dopamine, determined by positron emission tomography with 6-[18F]-fluoro-L-dopa, and compared them with changes in dopamine synthesis and storage ability, quantified with the fluorodopa uptake rate constant Ki. Six healthy subjects, 9 early PD patients (PD1), and 13 advanced PD patients (PD2) participated in the study. In the caudate, the Ki and EDV for PD1 were not significantly different from the normal values, whereas in the putamen Ki was 63% of normal and EDV was only 35%. Between PDI and PD2 the decline in EDV was higher than that for Ki (caudate 44% and putamen 46% for EDV vs. 21% and 34%, respectively, for Ki). Turnover was higher in the caudate than the putamen in controls, whereas the PD patients exhibited the reverse pattern. This comparison of changes in Ki and EDV as a function of disease progression indicates that a relatively slower decrease in dopamine synthesis and a relatively faster increase in turnover in early disease likely act as compensatory mechanisms, and that the clinical onset of PD reflects a global failure of dopaminergic compensatory mechanisms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 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".