Clinical Overlap between Jakob-Creutzfeldt Disease and Lewy Body Disease
Bibliographic record
Abstract
OBJECTIVE: Sporadic Jakob-Creutzfeldt disease (sCJD) and dementia with Lewy bodies (DLB) have overlapping clinical symptoms that can lead to their misdiagnosis. We delineated the clinical overlap between sCJD and DLB, and assessed the value of magnetic resonance imaging (MRI) to differentiate between them. METHODS: Medical records, MRI, electroencephalogram (EEG) and cerebrospinal fluid (CSF) were reviewed from 56 sCJD and 30 DLB subjects. RESULTS: 46% of sCJD subjects met probable DLB criteria and 40% of DLB subjects met probable CJD criteria. A greater proportion of sCJD subjects had cerebellar signs (66% vs. 10%, p<0.001), myoclonus (64% vs. 30%, p=0.002), and visual symptoms (other than hallucinations) (61% vs. 7%, p<0.001), whereas more DLB subjects had hallucinations (70% vs. 39%, p=0.007) and fluctuations (57% vs. 23%, p=0.002). Cortical and/or basal ganglia MRI diffusion weighted imaging hyperintensities consistent with sCJD were seen in 96% of sCJD subjects but in none with DLB. Logistic regression in sCJD revealed that those meeting probable DLB criteria were more likely to have occipital lobe involvement on MRI (OR 1.4, p=0.058, model p=0.022). Parietal lobe involvement on MRI was a predictor of "Other Focal Cortical signs" (OR 1.9, p=0.021). EEG and CSF assessments lacked sensitivity for sCJD as 48% of sCJD patients had a negative EEG; 67% of the 36 sCJD patents with a CSF evaluation had a negative or inconclusive 14-3-3 result. Too few DLB patients had EEG or CSF to assess their utility. CONCLUSION: Sporadic CJD and DLB have significant symptom overlap. MRI helps differentiate these diseases and is related to the signs/symptoms observed in sCJD.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".