MRI lesion profiles in sporadic Creutzfeldt–Jakob disease
Bibliographic record
Abstract
BACKGROUND: With respect to sporadic Creutzfeldt-Jakob disease (sCJD), six molecular subtypes (MM1, MM2, MV1, MV2, VV1, and VV2) have been described, which vary with respect to age at disease onset, disease duration, early symptoms, and neuropathology. MRI signal alterations were reported to correlate with distinct Creutzfeldt-Jakob disease (CJD) subtypes. This multicenter, international study aimed to describe the brain MRI findings associated with each of the sCJD molecular subtypes. METHODS: Pathologically confirmed sCJD cases with codon 129 genotype (MM, MV, and VV), PrP(Sc) type, and fluid-attenuated inversion recovery (FLAIR) or diffusion-weighted imaging (DWI) were collected in seven countries. All MRI scans were assessed for signal changes according to a standard protocol encompassing seven cortical regions, basal ganglia, thalamus, and cerebellum. RESULTS: MRI scans were evaluated in 211 CJD patients (98 MM1, 23 MM2, 19 MV1, 30 MV2, 9 VV1, and 32 VV2). Basal ganglia hyperintensities occurred most frequently in MV2, VV2, and MM1 subtypes (79, 77, and 70%). Wide cerebral cortical signal increase was most common in VV1, MM2, and MV1 subtypes (86, 77, and 77%). Thalamic hyperintensities occurred most often in VV2 (45%) and MV2 (43%). The most consistent finding across most subtypes was high signal in basal ganglia, with these abnormalities found in 63% (FLAIR) and 71% (DWI). CONCLUSION: Cortical signal increase and hyperintensities in the basal ganglia and thalamus are detected by MRI across all molecular sporadic Creutzfeldt-Jakob disease subtypes. Our findings argue that characteristic MRI lesion patterns may occur for each molecular subtype.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".