The advances in the early and accurate diagnosis of Creutzfeldt–Jakob disease and other prion diseases: where are we today?
Bibliographic record
Abstract
INTRODUCTION: Before the introduction of MRI diffusion-weighted images (DWI), the diagnosis of Creutzfeldt-Jakob disease (CJD) relied upon nonspecific findings including clinical symptoms, EEG abnormalities, and elevated levels of cerebrospinal fluid 14-3-3 protein. Subsequently, the use of DWI has improved diagnostic accuracy, but it sometimes remains difficult to differentiate CJD from encephalitis, epilepsy, and other dementing disorders. The revised diagnostic criteria include real-time quaking-induced conversion (RT-QuIC), detecting small amounts of CJD-specific prion protein, and clinically sensitive DWI. Combining these techniques has further improved diagnostic accuracy, enabling earlier diagnosis. AREAS COVERED: Herein, the authors review the recent advances in diagnostic methods and revised diagnostic criteria for sporadic CJD. They also discuss other prion diseases, such as variant CJD and chronic wasting disease, where the emergence of new types is a concern. EXPERT OPINION: Despite improvements in diagnostic methods and criteria, some subtypes of prion disease are still difficult to diagnose, and even the diagnosis using the most innovative RT-QuIC test remains a challenge in terms of accuracy and standardization. However, these revised criteria can be adapted to the emergence of new types of prion diseases. It is essential to continue careful surveillance and update information on the latest prion disease phenotypes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".