Evaluation of Six Commercial Mid- to High-Volume Antibody and Six Point-of-Care Lateral Flow Assays for Detection of SARS-CoV-2 Antibodies
Bibliographic record
Abstract
= 2]) was observed; however, overall specificity of EIAs was good (92 to 100%; all but one assay had specificity above 95%). POCTs were 0 to 100% sensitive >21 days after onset, with specificity ranging from 96 to 100%. However, many POCTs had faint banding and were often difficult to interpret. Serology assays can detect SARS-CoV-2 antibodies as early as 10 days after symptom onset. Serology assays vary in their sensitivity based on the marker (IgA/IgM versus IgG versus total) and by manufacturer; however, overall only 4 EIAs and 4 POCTs had sensitivities of >95% >21 days after symptom onset. Cross-reactivity with other seasonal coronaviruses is of concern. Serology assays should not be used for the diagnosis of acute infection but rather in carefully designed serosurveys to facilitate understanding of seroprevalence in a population and to identify previous exposure to SARS-CoV-2.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".