Enhanced Detection of Diffusion Reductions in Creutzfeldt-Jakob Disease at a Higher B Factor
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Diffusion-weighted imaging (DWI) is sensitive to the cerebral manifestations of human prion diseases. The magnitude of diffusion weighting, termed "b factor," has only been evaluated at the standard b = 1000 s/mm(2). This is the first rigorous evaluation of b = 2000 s/mm(2) in Creutzfeldt-Jakob Disease (CJD). MATERIALS AND METHODS: We compared DWI characteristics of 13 patients with CJD and 15 healthy controls at b = 1000 s/mm(2) and b = 2000 s/mm(2). Apparent diffusion coefficients (ADC) were computed and analyzed for the whole brain by voxel-wise analysis (by SPM5) as well as in anatomically defined volumes of interest (by FSL FIRST). RESULTS: Measured ADC was significantly lower (by approximately 5%-15%) at b = 2000 s/mm(2) than at b = 1000 s/mm(2) and significantly lower in patients than in controls. The differences between patients and controls were greater and more extensive at b = 2000 s/mm(2) than at b = 1000 s/mm(2) in the expected regions (thalamus, putamen, and caudate nucleus). CONCLUSIONS: Because higher b factors change the absolute value of observed ADC, as well as lesion detection, care should be taken when combining studies using different b factors. While the clinical application of high b factors is currently limited by a low signal intensity-to-noise ratio, it may offer more information in questionable cases, and our results confirm and extend the central role of diffusion imaging in human prion diseases.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".