Investigating SARS-CoV-2 Clinical Characteristics, Antibody Response, and Public Understanding of SARS-CoV-2 Laboratory Testing: Implications for COVID-19 Patient Management
Bibliographic record
Abstract
Variability in illness severity between patients with Coronavirus disease 2019 (COVID-19) may be attributed in part to differences in patient characteristics and the antibody response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Here, we investigated the interplay between these variables by characterizing risk factors for clinical manifestations and hospitalization, and by assessing the SARS-CoV-2 antibody response in a cohort of COVID-19 patients enrolled in the GENCOV study. Secondary to this, we evaluated participants’ understanding of SARS-CoV-2 laboratory testing to highlight deficits in knowledge and associations with sociodemographic factors. We identified associations between patient factors and 1) hospitalization, 2) clinical manifestations, 3) vaccine reactogenicity, and 4) the SARS-CoV-2 antibody response. We also identified knowledge gaps related to SARS-CoV-2 laboratory testing and results, and associations between knowledge and sociodemographic factors. The findings presented here serve to improve COVID-19 patient management strategies and inform future vaccine development and rollout plans.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".