Bibliographic record
Abstract
Abstract The diagnosis of multiple system atrophy and its differentiation from Parkinson's disease can be challenging, particularly in early disease. Both structural and functional imaging studies can be helpful for making the diagnosis, and could potentially be used to monitor disease progression. Magnetic resonance imaging can show distinctive patterns of atrophy and signal changes in multiple system atrophy, such as the “hot cross bun” and hyperintense putaminal rim signs, which are relatively specific, but have low sensitivity. Diffusivity might be particularly helpful in differentiating multiple system atrophy from Parkinson's disease, and appears to change over time, although the correlation with changes in motor dysfunction is uncertain. Diffusion weighted imaging changes in the middle cerebellar peduncle can also reliably separate multiple system atrophy from progressive supranuclear palsy. Volumetry detects characteristic patterns of atrophy. Alterations in spectroscopic patterns and abnormal iron deposition have also been reported. Positron emission tomography or single‐photon emission computed tomography measures of striatal dopamine innervation are abnormal in multiple system atrophy and Parkinson's disease; the addition of dopamine D2 receptor imaging (preserved in Parkinson's disease) can be helpful in differentiating the two. However, fluorodeoxyglucose positron emission tomography shows different characteristic metabolic networks in the two conditions that change with disease progression, and is probably more useful. Functional imaging can also be used to detect changes in cholinergic innervation in multiple system atrophy, and to study neuroinflammation. Imaging of cardiac sympathetic innervation can also differentiate between Parkinson's disease and multiple system atrophy, and might be underutilized for this purpose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".