Advances in the Role of Neuroimaging to Monitor Disease Progression in Parkinson’s Disease
Bibliographic record
Abstract
Since structural imaging has generally failed to demonstrate consistent abnormalities in Parkinson’s disease (PD), from an imaging perspective, the diagnosis has typically been based upon the demonstration of impaired striatal dopamine (DA) function. Radiotracer imaging techniques such as positron emission tomography (PET) and single photon emission computerised tomography (SPECT) allow the in vivo assessment of nigrostriatal DA function as well as regional cerebral blood flow, glucose metabolism, and functional connectivity, and changes in these measures have been used to infer disease progression. Pre-synaptic radiotracer imaging (RTI) has shown that striatal dopaminergic hypofunction follows a negative exponential pattern with the fastest rate of decline in early disease. Moreover, while striatal subregions remain differentially affected throughout the course of disease, with the posterior putamen affected more than anterior structures, the rate of deterioration is similar in all subregions. However, although functional imaging is undoubtedly a very useful tool in the assessment of PD progression, various studies have shown discordance between clinical progression of PD and nigrostriatal degeneration estimated by PET or SPECT. Therefore, considerable caution is warranted in the interpretation of imaging findings. While a potentially invaluable complement in assessing the severity of dopaminergic dysfunction, functional imaging is not a substitute for clinical assessment and other objective measures of PD severity, and cannot be currently considered a biomarker for progression of PD.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".