IFCC Interim Guidelines on Serological Testing of Antibodies against SARS-CoV-2
Bibliographic record
Abstract
Serological testing for the detection of antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is emerging as an important component of the clinical management of patients with coronavirus disease 2019 (COVID-19) as well as the epidemiological assessment of SARS-CoV-2 exposure worldwide. In addition to molecular testing for the detection of SARS-CoV-2 infection, clinical laboratories have also needed to increase testing capacity to include serological evaluation of patients with suspected or known COVID-19. While regulatory approved serological immunoassays are now widely available from diagnostic manufacturers globally, there is significant debate regarding the clinical utility of these tests, as well as their clinical and analytical performance requirements prior to application. This document by the International Federation for Clinical Chemistry and Laboratory Medicine (IFCC) Taskforce on COVID-19 provides interim guidance on: (A) clinical indications and target populations, (B) assay selection, (C) assay evaluation, and (D) test interpretation and limitations for serological testing of antibodies against SARS-CoV-2 infection. These evidence-based recommendations will provide practical guidance to clinical laboratories in the selection, verification, and implementation of serological assays and are of the utmost importance as we expand our pandemic response from initial case tracing and containment to mitigation strategies to minimize resurgence and further morbidity and mortality.
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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 | 0.073 | 0.100 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.022 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.014 |
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".