Stringent thresholds for SARS-CoV-2 IgG assays result in under-detection of cases reporting loss of taste/smell
Bibliographic record
Abstract
Abstract Thresholds for SARS-CoV-2 antibody assays have typically been determined using samples from symptomatic, often hospitalised, patients. Assay performance following mild/asymptomatic infection is unclear. We assessed IgG responses in asymptomatic healthcare workers with a high pre-test probability of Covid-19, e.g. 807/9292(8.9%) reported loss of smell/taste. The proportion reporting anosmia/ageusia increased at antibody titres below diagnostic thresholds for both an in-house ELISA and the Abbott Architect chemiluminescent microparticle immunoassay (CMIA): 424/903(47%) reported anosmia/ageusia with a positive ELISA, 59/387(13.2%) with high-negative titres, and 324/7943(4.1%) with low-negative results. Adjusting for the proportion of staff reporting anosmia/ageusia suggests the sensitivity of both assays is lower than previously reported: Oxford ELISA 90.8% (95%CI 86.1-92.1%) and Abbott CMIA 80.9% (77.5-84.3%). However, the sensitivity may be lower if some anosmia/ageusia in those with low-negative titres is Covid-19-associated. Samples from individuals with mild/asymptomatic infection should be included in SARS-CoV-2 immunoassay evaluations. Reporting equivocal SARS-CoV-2 antibody results should be considered.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".