A Reversible Tracer Analysis Approach to the Study of Effective Dopamine Turnover
Bibliographic record
Abstract
Changes in dopamine turnover resulting from disease states such as Parkinson's disease may be reflected in corresponding changes in the kinetics of the positron emission tomographic tracer [(18)F]fluorodopa. The authors had previously refined the conventional irreversible-tracer graphical approach to determine both the uptake rate constant K(i) and the rate constant kloss that describes the slow loss of the trapped kinetic component. Because these parameters change in the opposite sense with disease, their ratios may be more powerfully discriminating than either one alone. The ratio k(loss)/K(i) is indicative of effective dopamine turnover. Its inverse, K(i)/k(loss), can be interpreted as the effective distribution volume (EDV) of the specific uptake compartment referred to the fluorodopa concentration in plasma. Here the authors present a new approach to the estimation of EDV based on reversible-tracer graphical methods. When implemented with a plasma input function, the method evaluates EDV directly. When implemented with a tissue input function, the outcome is proportional to the ratio of the distribution volumes of the specific uptake and precursor compartments. Comparison of the new and previous approaches strongly validates this alternative approach to the study of effective dopamine turnover.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".