Assessment of JC virus DNA in blood and urine from natalizumab‐treated patients
Bibliographic record
Abstract
OBJECTIVE: Analyses were conducted to determine the clinical utility of measuring JC virus (JCV) DNA in blood or urine of natalizumab-treated multiple sclerosis (MS) patients to predict the risk of progressive multifocal leukoencephalopathy (PML). METHODS: A total of 12,850 blood and urine samples from nearly 1,400 patients participating in natalizumab clinical trials were tested for JCV DNA using a commercially available quantitative polymerase chain reaction (qPCR) assay. A subset of these samples was also tested using a more sensitive qPCR assay developed at the National Institutes of Health (NIH). RESULTS: At the time natalizumab dosing was suspended, JCV DNA was detected in plasma by the commercial assay in 4 of 1,397 (0.3%) patients; the NIH assay confirmed these positive samples and detected JCV DNA in an additional 2 of 205 (1%) patients who tested negative with the commercial assay. None of these 6 JCV DNA positive patients developed PML. In a 48-week study testing the safety of natalizumab redosing, JCV DNA was detected in plasma of 6 of 1,094 (0.3%) patients, none of whom developed PML. Urine at baseline and week 48 was assessed in 224 patients; 58 (26%) were positive at baseline, and 55 (25%) were positive after 48 weeks of natalizumab, treatment. JCV DNA was not detected in peripheral blood mononuclear cells from any of these 1,094 patients before or after natalizumab treatment. In 5 patients who developed PML, JCV DNA was not detected in blood at any time point before symptoms first occurred. INTERPRETATION: Measuring JCV DNA in blood or urine with currently available methods is unlikely to be useful for predicting PML risk in natalizumab-treated MS patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".