IFCC interim guidelines on rapid point-of-care antigen testing for SARS-CoV-2 detection in asymptomatic and symptomatic individuals
Bibliographic record
Abstract
With an almost unremittent progression of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections all around the world, there is a compelling need to introduce rapid, reliable, and high-throughput testing to allow appropriate clinical management and/or timely isolation of infected individuals. Although nucleic acid amplification testing (NAAT) remains the gold standard for detecting and theoretically quantifying SARS-CoV-2 mRNA in various specimen types, antigen assays may be considered a suitable alternative, under specific circumstances. Rapid antigen tests are meant to detect viral antigen proteins in biological specimens (e.g. nasal, nasopharyngeal, saliva), to indicate current SARS-CoV-2 infection. The available assay methodology includes rapid chromatographic immunoassays, used at the point-of-care, which carries some advantages and drawbacks compared to more conventional, instrumentation-based, laboratory immunoassays. Therefore, this document by the International Federation for Clinical Chemistry and Laboratory Medicine (IFCC) Taskforce on COVID-19 aims to summarize available data on the performance of currently available SARS-CoV-2 antigen rapid detection tests (Ag-RDTs), providing interim guidance on clinical indications and target populations, assay selection, and evaluation, test interpretation and limitations, as well as on pre-analytical considerations. This document is hence mainly aimed to assist laboratory and regulated health professionals in selecting, validating, and implementing regulatory approved Ag-RDTs.
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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 | 0.037 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.012 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.013 |
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".