Use of diagnostics for serosurveillance studies in pregnant individuals during the SARS-CoV-2 pandemic
Bibliographic record
Abstract
Pregnant individuals are known to be at increased risk of worse outcomes from COVID-19 infection. Recent data suggests that they are also more likely to exhibit milder symptoms and have higher rates of asymptomatic infection. The health impacts of milder disease are less well-described but may include adverse perinatal outcomes. Serosurveillance can help describe accurately background rates of seropositivity in populations with high rates of asymptomatic infection. The seroprevalence of SARS-CoV-2 infection prior to vaccine availability was assessed in two large maternity centres. Of 437 pregnant individuals, seven were positive on initial screening, with one false positive identified on subsequent confirmatory testing. An overall seropositivity rate of 1.4% was found. No adverse pregnancy outcomes were identified. Confirmatory testing was performed with four commercial antibody-based assays and an in-house microneutralisation assay. Wantai SARS-CoV-2 receptor-binding-domain total antibody performed best, similar to previous reports. Serological surveillance can estimate infection rates not captured by acute PCR testing, and may assist with contact tracing, estimation of immunity rates and inform public health policy in at-risk groups. Evaluation of serological assays should be integrated into serosurveillance initiatives.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".