A Case of Sporadic Creutzfeldt-Jakob Disease (sCJD) with Lightning-like Progression
Bibliographic record
Abstract
Background: RT-QuIC (Real-Time QuIC) is a clinical diagnostic method that detects trace proteins in samples through protein amplification. According to the international CJD diagnostic criteria, RT-QuIC provides diagnostic evidence that is second only to that of pathology. Presently, this approach remains underutilized in China. Case Report: A 76-year-old female patient was admitted to the hospital for a period of two weeks, characterized by a general slowing of her cognitive responses. A physical examination revealed elevated levels of cognitive impairment, as indicated by a Mini-Mental State Examination (MMSE) score of 12 and a Montreal Cognitive Assessment (MoCA) score of 8. An electroencephalogram (EEG) revealed moderate abnormalities, characterized by intermittent high-amplitude delta waves (frequency 3–3.5 Hz) across multiple leads and short-range rhythmic discharges in several leads. A magnetic resonance imaging (MRI) scan revealed the presence of ribbon-like changes in the parietal and temporal cortical regions on diffusion-weighted imaging (DWI) sequences. CSF testing for 14-3-3 protein yielded a negative result, while RT-QuIC testing produced a positive result. Treatment: The primary management strategy encompassed the provision of supportive care and the management of symptoms. The clinical outcome is as follows: The treatment response was unsatisfactory, with a rapid disease progression over a 52-day period. The patient exhibited a progressive deterioration in visuospatial function, accompanied by the emergence of myoclonus, tremor, urinary and fecal incontinence, and motor mutism. Conclusion: RT-QuIC testing has the potential to enhance diagnostic specificity and sensitivity in patients suspected of having Creutzfeldt-Jakob disease (CJD).
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".