Multimodality Brain Imaging Markers in <scp>Progressive Supranuclear Palsy</scp> Subtypes and Parkinson's Disease
Bibliographic record
Abstract
Abstract Background The new classification of progressive supranuclear palsy (PSP) subtypes necessitates identifying radiological biomarkers to support the clinical diagnosis. Objective The goal was to test if magnetic resonance imaging (MRI) morphometry, diffusion tensor imaging (DTI), susceptibility‐weighted imaging (SWI), or [18F]fluorodeoxyglucose ( 18F FDG)‐positron emission tomography (PET) differentiates PSP subtypes from each other or Parkinson's disease (PD). Methods Midbrain/pons (M/P) area ratio, middle/superior cerebellar peduncle (MCP/SCP) width ratio, magnetic resonance parkinsonism indices (MRPI and MRPI2) and midbrain antero‐posterior (AP) diameter were measured. Region of interest‐based DTI, SWI, and 18F FDG‐PET analyses were performed. Results Four PSP subtypes (n = 85) and 24 PD were studied. MRI morphometry and DTI could differentiate PSP‐Richardson syndrome (PSP‐RS) from PSP‐parkinsonism, PSP‐postural instability, and PD (area under curve >0.7). SWI did not differentiate among PSP subtypes or PD. 18F FDG‐PET distinguished PSP from PD. Conclusions MRI morphometry and DTI differentiated PSP‐RS from the other common PSP subtypes and PD and may be tested as a radiological marker of PSP‐RS in larger studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".