Applications of SARS-CoV-2 serological testing: impact of test performance, sample matrices, and patient characteristics
Bibliographic record
Abstract
Laboratory testing has been a key tool in managing the SARS-CoV-2 global pandemic. While rapid antigen and PCR testing has proven useful for diagnosing acute SARS-CoV-2 infections, additional testing methods are required to understand the long-term impact of SARS-CoV-2 infections on immune response. Serological testing, a well-documented laboratory practice, measures the presence of antibodies in a sample to uncover information about host immunity. Although proposed applications of serological testing for clinical use have previously been limited, current research into SARS-CoV-2 has shown growing utility for serological methods in these settings. To name a few, serological testing has been used to identify patients with past infections and long-term active disease and to monitor vaccine efficacy. Test utility and result interpretation, however, are often complicated by factors that include poor test sensitivity early in infection, lack of immune response in some individuals, overlying infection and vaccination responses, lack of standardization of antibody titers/levels between instruments, unknown titers that confer immune protection, and large between-individual biological variation following infection or vaccination. Thus, the three major components of this review will examine (1) factors that affect serological test utility: test performance, testing matrices, seroprevalence concerns and viral variants, (2) patient factors that affect serological response: timing of sampling, age, sex, body mass index, immunosuppression and vaccination, and (3) informative applications of serological testing: identifying past infection, immune surveillance to guide health practices, and examination of protective immunity. SARS-CoV-2 serological testing should be beneficial for clinical care if it is implemented appropriately. However, as with other laboratory developed tests, use of SARS-CoV-2 serology as a testing modality warrants careful consideration of testing limitations and evaluation of its clinical utility.
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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 | 0.004 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| 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".