Effect of Dopamine Loss and the Metabolite 3- <i>O</i> -Methyl-[ <sup>18</sup> F]Fluoro-dopa on the Relation between the <sup>18</sup> F-Fluorodopa Tissue Input Uptake Rate Constant K <sub>occ</sub> and the [ <sup>18</sup> F]Fluorodopa Plasma Input Uptake Rate Constant K <sub>i</sub>
Bibliographic record
Abstract
Parkinson disease is characterized by the loss of dopaminergic neurons, thus decreasing the system's ability to produce and store dopamine (DA). Such ability is often investigated using 18F-fluorodopa (FD) positron emission tomography. A commonly used model to investigate the DA synthesis and storage rate is the modified Patlak graphical approach. This approach allows for both plasma and tissue input functions, yielding the respective uptake rate constants K(i) and K(occ). This method requires the presence of an irreversible compartment and the absence of any nontrapped tracer metabolite. In the case of K(occ), this last assumption is violated by the presence of the FD metabolite 3-O-methyl-[18F]fluoro-dopa (3OMFD), which makes the K(occ) evaluation susceptible to a downward bias. It was found that both K(i) and K(occ) are influenced by DA loss and thus are not pure measures of DA synthesis and storage. In the case of K(occ), the presence of 3OMFD exacerbates the effect of DA egress, thus introducing a disease-dependent bias in the K(occ) determination. These findings imply that K(i) and K(occ) provide different assessments of disease severity and that, as disease progresses, K(i) and especially K(occ) become more related to DA storage capacity and less to the DA synthesis rate.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".