Interlaboratory Comparison of Cytomegalovirus Viral Load Assays
Bibliographic record
Abstract
To assess interlaboratory variability in qualitative and quantitative cytomegalovirus (CMV) viral load (VL) testing, we distributed a panel of samples to 33 laboratories in the USA, Canada and Europe who performed testing using commercial reagents (n = 17) or laboratory-developed assays (n = 18). The panel included two negatives, seven samples constructed from purified CMV nucleocapsids in plasma (2.0-6.0 log(10) copies/mL) and three clinical plasma samples. Interlaboratory variation was observed in both actual (range, 2.0-4.0 log(10) copies/mL) and self-reported lower limits of detection (range, 1.0-4.0 log(10) copies/mL). Variation observed in reported results for individual samples ranged from 2.0 log(10) (minimum) to 4.3 log(10) (maximum)(.) Variation was greatest at low VLs. Assuming +/- 0.5 log(10) relative to the expected result represents an acceptable result, 57.6% of results fell within this range. Use of commercially available reagents and procedures was associated with less variability compared with laboratory-developed assays. Interlaboratory variability on replicate samples was significantly greater than intralaboratory variability (p < 0.0001). The significant interlaboratory variability in CMV VL observed may be impacting patient care and limiting interinstitutional comparisons. The creation of an international reference standard for CMV VL assay calibration would be an important step in quality improvement of this laboratory tool.
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 | 0.060 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".