Evaluation of Three Cytomegalovirus IgG Lateral Flow Assays for Rapid Determination of CMV Serostatus
Bibliographic record
Abstract
Background: Cytomegalovirus (CMV) serostatus is a major determinant of CMV infection, disease risk, and transplant outcomes. Current clinical serology assays are limited by relatively slow turnaround time, design for batched testing, need for trained personnel, and/or specialized equipment. Rapid diagnostic assays in development have a role in emerging settings, such as critically ill patients, but have not been systematically evaluated. Methods: We assessed the performance of 3 rapid lateral flow assays (LFAs) for the detection of CMV immunoglobulin (Ig)G antibodies compared with a reference commercially available CMV IgG enzyme-linked immunosorbent assay in residual serum samples from 200 consecutive adults who underwent clinical CMV serology testing. Samples with discrepant results between the LFA and reference assay were tested by a second reference assay. A subset of serum samples was assessed for interoperator variability. Operating characteristics of the QooLabs LFA were separately assessed in plasma samples. Results: The sensitivity and specificity of the individual LFA assays using serum varied significantly: 86%/83%, 99/93%, and 57/97%, for Healgen, QNow automated reader, and nanoComposix, respectively, compared with the reference assay. Results for the QNow assay were comparable between automated and manual reads. Among a subset of 10 serum samples assessed by 5 individual operators, 44 of 50 (88%) results were concordant. Among 50 plasma samples assessed by the QooLabs LFA, the sensitivity and specificity were 72% and 96%. Conclusions: The ease of performance, rapid turnaround time, and good operating characteristics provide the rationale for further evaluation of the Qoolabs QNow LFA in specialized settings where rapid assessment of CMV serostatus would be advantageous.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".