Infectious Diseases Society of America Guidelines on the Diagnosis of COVID-19: Serologic Testing (September 2020)
Bibliographic record
Abstract
BACKGROUND: The availability of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serologic testing has rapidly increased. Current assays use a variety of technologies, measure different classes of immunoglobulin or immunoglobulin combinations and detect antibodies directed against different portions of the virus. The overall accuracy of these tests, however, has not been well-defined. The Infectious Diseases Society of America (IDSA) convened an expert panel to perform a systematic review of the coronavirus disease 2019 (COVID-19) serology literature and construct best practice guidance related to SARS-CoV-2 serologic testing. This guideline is the fourth in a series of rapid, frequently updated COVID-19 guidelines developed by IDSA. OBJECTIVE: IDSA's goal was to develop evidence-based recommendations that assist clinicians, clinical laboratories, patients and policymakers in decisions related to the optimal use of SARS-CoV-2 serologic tests in a variety of settings. We also highlight important unmet research needs pertaining to the use of anti-SARS-CoV-2 antibody tests for diagnosis, public health surveillance, vaccine development and the selection of convalescent plasma donors. METHODS: A multidisciplinary panel of infectious diseases clinicians, clinical microbiologists and experts in systematic literature review identified and prioritized clinical questions related to the use of SARS-CoV-2 serologic tests. Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology was used to assess the certainty of evidence and make testing recommendations. RESULTS: The panel agreed on eight diagnostic recommendations. CONCLUSIONS: Information on the clinical performance and utility of SARS-CoV-2 serologic tests are rapidly emerging. Based on available evidence, detection of anti-SARS-CoV-2 antibodies may be useful for confirming the presence of current or past infection in selected situations. The panel identified three potential indications for serologic testing including: 1) evaluation of patients with a high clinical suspicion for COVID-19 when molecular diagnostic testing is negative and at least two weeks have passed since symptom onset; 2) assessment of multisystem inflammatory syndrome in children; and 3) for conducting serosurveillance studies. The certainty of available evidence supporting the use of serology for either diagnosis or epidemiology was, however, graded as very low to moderate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".