P.009 Improving Detection of Creutzfeldt-Jakob Disease Mimics in Clinical Practice
Bibliographic record
Abstract
Background: Assays capable of detecting prions in CSF (e.g., RT-QuIC) have greatly improved the antemortem diagnosis of Creutzfeldt-Jakob disease (CJD) yet take time to conduct and are not widely accessible. There is a need to identify clinical features and common tests that identify mimics at presentation. Methods: Mimics were identified within longitudinal studies of rapidly progressive dementia at study sites. Mimics met clinical criteria for probable CJD but did not have CJD. Clinical features were compared between mimics and patients with CJD assessed at Mayo Clinic Enterprise (n=79) and Washington University in St. Louis (n=10; Jan-2014 to Oct-2020). Results: Mimics (10/155; 6.5%) were diagnosed with autoimmune encephalitis (n=7), neurosarcoidosis, frontotemporal lobar degeneration with motor neuron disease, and unknown dementia. Age-at-symptom onset, gender, presenting symptoms, and EEG and MRI findings were similar between mimics and CJD patients. Focal motor abnormalities (49/93, 10/10), elevations in CSF leukocytosis (4/92, 5/10) and protein (39/92, 9/10) were more common in mimics (p<0.01). Neural-specific autoantibodies associated with autoimmune encephalitis were detected within the serum (4/9) and CSF (5/10) of mimics, but not CJD cases. Conclusions: Autoimmune encephalitis, neurosarcoidosis and neurodegenerative diseases may mimic CJD at presentation and should be considered in patients with early motor dysfunction and abnormal CSF 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 | 0.003 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".