Assessment of the longitudinal humoral response in non-hospitalized SARS-CoV-2-positive individuals at decentralized sites: Outcomes and concordance
Bibliographic record
Abstract
Introduction: Early in the COVID-19 pandemic, reagent availability was not uniform, and infrastructure had to be urgently adapted to undertake COVID-19 surveillance. Methods: Before the validation of centralized testing, two enzyme-linked immunosorbent assays (ELISA) were established independently at two decentralized sites using different reagents and instrumentation. We compared the results of these assays to assess the longitudinal humoral response of SARS-CoV-2-positive (i.e., PCR-confirmed), non-hospitalized individuals with mild to moderate symptoms, who had contracted SARSCoV-2 prior to the appearance of variants of concern in Québec, Canada. Results: The two assays exhibited a high degree of concordance to identify seropositive individuals, thus validating the robustness of the methods. The results also confirmed that serum immunoglobulins persist ≥ 6 months post-infection among non-hospitalized adults and that the antibodies elicited by infection cross-reacted with the antigens from P.1 (Gamma) and B.1.617.2 (Delta) variants of concern. Discussion: Together, these results demonstrate that immune surveillance assays can be rapidly and reliably established when centralized testing is not available or not yet validated, allowing for robust immune surveillance.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".