Standardization of Hemagglutination Inhibition Assay for Influenza Serology Allows for High Reproducibility between Laboratories
Bibliographic record
Abstract
Standardization of the hemagglutination inhibition (HAI) assay for influenza serology is challenging. Poor reproducibility of HAI results from one laboratory to another is widely cited, limiting comparisons between candidate vaccines in different clinical trials and posing challenges for licensing authorities. In this study, we standardized HAI assay materials, methods, and interpretive criteria across five geographically dispersed laboratories of a multidisciplinary influenza research network and then evaluated intralaboratory and interlaboratory variations in HAI titers by repeatedly testing standardized panels of human serum samples. Duplicate precision and reproducibility from comparisons between assays within laboratories were 99.8% (99.2% to 100%) and 98.0% (93.3% to 100%), respectively. The results for 98.9% (95% to 100%) of the samples were within 2-fold of all-laboratory consensus titers, and the results for 94.3% (85% to 100%) of the samples were within 2-fold of our reference laboratory data. Low-titer samples showed the greatest variability in comparisons between assays and between sites. Classification of seroprotection (titer ≥ 40) was accurate in 93.6% or 89.5% of cases in comparison to the consensus or reference laboratory classification, respectively. This study showed that with carefully chosen standardization processes, high reproducibility of HAI results between laboratories is indeed achievable.
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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 | 0.071 | 0.067 |
| 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 | 0.002 |
| 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".