Results from the second WHO external quality assessment for the molecular detection of respiratory syncytial virus, 2019–2020
Bibliographic record
Abstract
Background: External quality assessments (EQAs) for the molecular detection of human respiratory syncytial virus (RSV) are necessary to ensure the standardisation of reliable results. The Phase II, 2019-2020 World Health Organization (WHO) RSV EQA included 28 laboratories in 26 countries. The EQA panel evaluated performance in the molecular detection and subtyping of RSV-A and RSV-B. This manuscript describes the preparation, distribution, and analysis of the 2019-2020 WHO RSV EQA. Methods: Panel isolates underwent whole genome sequencing and in silico primer matching. The final panel included nine contemporary, one historical virus and two negative controls. The EQA panel was manufactured and distributed by the UK National External Quality Assessment Service (UK NEQAS). National laboratories used WHO reference assays developed by the United States Centers for Disease Control and Prevention, an RSV subtyping assay developed by the Victorian Infectious Diseases Reference Laboratory (Australia), or other in-house or commercial assays already in use at their laboratories. Results: An in silico analysis of isolates showed a good match to assay primer/probes. The panel was distributed to 28 laboratories. Isolates were correctly identified in 98% of samples for detection and 99.6% for subtyping. Conclusions: The WHO RSV EQA 2019-2020 showed that laboratories performed at high standards. Updating the composition of RSV molecular EQAs with contemporary strains to ensure representation of circulating strains, and ensuring primer matching with EQA panel viruses, is advantageous in assessing diagnostic competencies of laboratories. Ongoing EQAs are recommended because of continued evolution of mismatches between current circulating strains and existing primer sets.
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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 | 0.025 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".