Bibliographic record
Abstract
Functional imaging may be particularly helpful for the assessment of levodopa (l-dopa) response and long-term complications of therapy in Parkinson's disease. Radiotracer imaging allows the quantitative determination of regional changes in blood flow and glucose metabolism, as well as alterations in brain connectivity and network activation and changes in dopamine receptors, non-dopaminergic neurotransmitter systems, and to a lesser extent, signaling pathways downstream to dopamine receptors. The focus of the present article, however, is the application of positron emission tomography (PET) to study the central pharmacokinetics of l-dopa. Radioligands with limited affinity for the dopamine D2 receptor are sensitive to changes in the levels of synaptic dopamine and can accordingly provide helpful insights into the magnitude and time course of dopamine release after l-dopa. Prolonged fluorodopa PET scans can be used to estimate the rate of dopamine turnover. Studies performed with these techniques have demonstrated increased dopamine turnover and increased but shorter duration release of dopamine after l-dopa as Parkinson's disease (PD) progresses, increased release of dopamine in patients with l-dopa-induced dyskinesia, and that aberrant patterns of dopamine release may actually predict the future development of motor fluctuations. Taken together, the studies provide in vivo validation for the hypothesis that pulsatile stimulation of dopamine receptors plays a critical role in the emergence of long-term motor complications of therapy. Similar approaches can be used to study the non-motor complications of PD and its treatment. Society.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".