Quality control of multiplex antibody detection in samples from large-scale surveys: the example of malaria in Haiti
Bibliographic record
Abstract
Measuring antimalarial antibodies can estimate transmission in a population. To compare outputs, standardized laboratory testing is required. Here we describe the in-country establishment and quality control (QC) of a multiplex bead assay (MBA) for three sero-surveys in Haiti. Total IgG data against 21 antigens were collected for 32,758 participants. Titration curves of hyperimmune sera were included on assay plates, assay signals underwent 5-parameter regression, and inspection of the median and interquartile range (IQR) for the y-inflection point was used to determine assay precision. The medians and IQRs were similar for Surveys 1 and 2 for most antigens, while the IQRs increased for some antigens in Survey 3. Levey-Jennings charts for selected antigens provided a pass/fail criterion for each assay plate and, of 387 assay plates, 13 (3.4%) were repeated. Individual samples failed if IgG binding to the generic glutathione-S-transferase protein was observed, with 659 (2.0%) samples failing. An additional 455 (1.4%) observations failed due to low bead numbers (<20/analyte). The final dataset included 609,438 anti-malaria IgG data points from 32,099 participants; 96.6% of all potential data points if no QC failures had occurred. The MBA can be deployed with high-throughput data collection and low inter-plate variability while ensuring data quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".