Advances in the Role of Neuroimaging to Monitor Disease Progression in Parkinson’s Disease
Why this work is in the frame
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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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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 | 0.001 |
| 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 | 0.000 |
| 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 it