Detection of SARS-CoV-2 RNA and Antibodies in Diverse Samples: Protocol to Validate the Sufficiency of Provider-Observed, Home-Collected Blood, Saliva, and Oropharyngeal Samples
Bibliographic record
Abstract
BACKGROUND: The response in the United States to the coronavirus disease (COVID-19) pandemic has been hampered by a lack of aggressive testing for the infection. Testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cornerstone of an effective public health response. However, efforts to test have been hampered by limited reagents, limitations in the availability of swabs used for the collection of nasopharyngeal swab (NPS) specimens, limitations in personal protective equipment (PPE) for health care providers collecting the NPS specimens, and limitations in viral transport media for transporting the specimens. Therefore, more flexible options for screening for SARS-CoV-2 RNA and serologic responses are critical to inform clinical and public health responses. OBJECTIVE: We aim to document the ability of patients to self-collect sufficient specimens for SARS-CoV-2 viral detection and serology. METHODS: Patient self-collection of samples will be done with observation by a health care provider during a telemedicine session. Participants will be mailed a specimen collection kit, engage in a telehealth session with a provider through a HIPPA (Health Insurance Portability and Accountability Act of 1996)-compliant video meeting, and collect specimens while being observed by the provider. Providers will record whether they are confident in the suitability of the specimen for laboratory testing that would inform clinical decision making. We will objectively assess the sufficiency of biological material in the mailed-in specimens. RESULTS: The protocol was approved by the Emory University Institutional Review Board (IRB) on March 30, 2020 (Protocol number 371). To date, we have enrolled 159 participants. CONCLUSIONS: Defining a conceptual framework for assessing the sufficiency of patient-collected samples for the detection of SARS-CoV-2 RNA and serologic responses to infection is critical for facilitating public health responses and providing PPE-sparing options to increase testing. Validation of alternative methods of specimen collection should include objective measures of the sufficiency of specimens for testing. A strong evidence base for diversifying testing modalities will improve tools to guide public health responses to the COVID-19 pandemic.
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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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".