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 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 | 0.003 | 0.071 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".