Diagnosing progressive multifocal leukoencephalopathy: Positive predictive value of CSF JC virus quantitative PCR and importance of recognizing suggestive neuroimaging findings
Bibliographic record
Abstract
OBJECTIVE: To determine the positive predictive value (PPV) of CSF John Cunningham virus (JCV) quantitative PCR (qPCR) for progressive multifocal leukoencephalopathy (PML), and highlight neuroimaging findings reported to be suggestive of this disease. METHODS: We reviewed patients at London Health Sciences Centre with a positive CSF JCV qPCR result. Patients were classified as true-positive if they had a clinico-radiographic presentation compatible with PML and no more likely alternative diagnosis. The presence of suggestive neuroimaging findings was documented as supportive evidence of PML. The PPV of CSF JCV qPCR was calculated as the proportion of positive results that were classified as true-positives. RESULTS: Eleven of 154 patients who underwent CSF JCV qPCR testing had a positive result (7 %). Median age was 60 years (range: 33-79 years) and 7/11 (64 %) were male. Nine of 11 (82 %) were overtly immunocompromised. Five of 11 (45 %) had a viral load below the lowest quantifiable standard (<4290 copies/ml). All had a clinico-radiographic presentation compatible with PML and no more likely alternative diagnosis, resulting in a PPV of 100 %. All had one or more suggestive neuroimaging findings that supported PML diagnosis (Milky Way sign/punctate pattern, 9; rim-and-core pattern, 7; T2/FLAIR mismatch, 6; shrimp sign, 4; SWI-hypointense rim, 2; across-the-pons sign, 1; barbell sign, 1). CONCLUSIONS: We found that CSF JCV qPCR had high PPV for PML. All positives below the lowest quantifiable standard were true-positives. Our study affirms the diagnostic utility of this testing in clinical practice. Recognition of suggestive neuroimaging findings helps facilitate PML diagnosis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".