Assay requirements for COVID-19 testing: serology vs. rapid antigen tests
Bibliographic record
Abstract
To the Editor, Apart from the molecular diagnostic PCR test (gold standard), there are two other types of SARS-COV-2-related tests that are fundamental for our battle against the COVID-19 pandemic: a. Serological tests measure host antibodies against SARS-COV-2 to delineate possible past infection.These tests can also be used to assess disease prevalence and monitor the dynamics of individual immunological responses over time [1].b.Rapid antigen tests measure SARS-COV-2 proteins to determine the putative COVID-19 contagiousness state.The usefulness of frequent COVID-19 antigen testing through inexpensive, simple and rapid tests has been established [2].These tests can contribute tremendously to COVID-19 infection control, even if their analytical sensitivity is two to three orders of magnitude lower than the benchmark PCR test [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 | 0.018 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".