Interlaboratory Comparison of Epstein-Barr Virus Viral Load Assays
Bibliographic record
Abstract
To assess interlaboratory variability in qualitative and quantitative Epstein-Barr virus (EBV) viral load (VL) testing, we distributed a panel of samples to 28 laboratories in the USA, Canada and Europe who performed testing using commercially available reagents (n = 12) or laboratory-developed assays (n = 18). The panel included two negatives, seven constructed samples using Namalwa and Molt-3 cell lines diluted in plasma (1.30-5.30 log(10) copies/mL) and three clinical plasma samples. Significant interlaboratory variation was observed for both actual (range 1.30-4.30 log(10) copies/mL) and self-reported (range, 1.70-3.30 log(10) copies/mL) lower limits of detection. The variation observed in reported results on individual samples ranged from 2.28 log(10) (minimum) to 4.14 log(10) (maximum). Variation was independent of dynamic range and use of commercial versus laboratory-developed assays. Overall, only 47.0% of all results fell within acceptable standards of variation: defined as the expected result +/- 0.50 log(10). Interlaboratory variability on replicate samples was significantly greater than intralaboratory variability (p < 0.0001). Kinetics of change in VL appears more relevant than absolute values and clinicians should understand the uncertainty associated with absolute VL values at their institutions. The creation of an international reference standard for EBV VL assay calibration would be an initial important step in quality improvement of this laboratory tool.
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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 | 0.022 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".