Characterizing the SARS-CoV-2 antibody response and associations with patient factors: Serological profiling of participants enrolled in the GENCOV study
Bibliographic record
Abstract
INTRODUCTION: The GENCOV study sought to evaluate serological differences between individuals with differing COVID-19 severity and outcomes. We assessed the SARS-CoV-2 antibody response of GENCOV participants cross-sectionally 1-, 6-, and 12-months following COVID-19 diagnosis to identify patient factors associated with more robust and durable humoral immune responses. MATERIALS AND METHODS: COVID-19 patients and a control cohort of vaccinated infection-naïve participants were recruited at hospital sites across the Greater Toronto Area in Ontario, Canada. Commercially available and laboratory-developed serological assays were used to characterize features of participants' antibody responses, including both binding and neutralizing antibodies. Regression analyses were performed to identify associations between participant characteristics and features of the SARS-CoV-2 antibody response. RESULTS: Samples were obtained from participants 1- (n = 938), 6- (n = 842), and 12-months (n = 662) post-infection or vaccination. At all time points, vaccinees, and to a greater extent those who were both infected and vaccinated, had significantly elevated anti-spike antibody levels compared to unvaccinated participants. Increasing age and/or illness severity were associated with significantly higher antibody levels among unvaccinated participants. Among vaccines, those who were vaccinated after infection (i.e., hybrid immunity) had consistently higher antibody levels compared to participants who were infection-naïve or vaccinated before their infection (i.e., breakthrough infections). Additionally, receiving more vaccine doses and having a more recent vaccination were strongly associated with higher antibody levels across all time points. CONCLUSIONS: Our findings highlight various patient factors, including vaccination, which contribute to robust, durable SARS-CoV-2 antibody responses. Overall, the findings presented here may 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".