AACC Practical Recommendations for Implementing and Interpreting SARS-CoV-2 Emergency Use Authorization and Laboratory-Developed Test Serologic Testing in Clinical Laboratories
Bibliographic record
Abstract
BACKGROUND: The clinical laboratory continues to play a critical role in managing the coronavirus pandemic. Numerous US Food and Drug Administration emergency use authorization (EUA) and laboratory-developed test (LDT) serologic assays have become available. The performance characteristics of these assays and their clinical utility continue to be defined in real time during this pandemic. The AACC convened a panel of experts from clinical chemistry, microbiology, and immunology laboratories; the in vitro diagnostics industry; and regulatory agencies to provide practical recommendations for implementation and interpretation of these serologic tests in clinical laboratories. CONTENT: The currently available EUA serologic tests and platforms, information on assay design, antibody classes including neutralizing antibodies, and the humoral immune responses to SARS-CoV-2 are discussed. Verification and validation of EUA and LDT assays are described, along with a quality management approach. Four indications for serologic testing are outlined. Recommendations for result interpretation, reporting comments, and the role of orthogonal testing are also presented. SUMMARY: This document aims to provide a comprehensive reference for laboratory professionals and healthcare workers to appropriately implement SARS-CoV-2 serologic assays in the clinical laboratory and to interpret test results during this pandemic. Given the more frequent occurrence of outbreaks associated with either vector-borne or respiratory pathogens, this document will be a useful resource in planning for similar scenarios in the future.
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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 | 0.061 | 0.141 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.015 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 0.025 |
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".