Serological responses to SARS-CoV-2 in vaccinated and unvaccinated individuals: A Canadian study
Bibliographic record
Abstract
• 159 Canadian individuals infected with COVID between March and Sept 2020 were studied. • Anti-Spike protein antibody responses and comorbidities were correlated with disease severity. • Older patients were more likely to have a response than younger patients. • Female patients over 60 years had more antibody responses compared those under 60 years. • Vaccinated patients had a stronger IgG response against S protein and a weaker response against N protein compared to unvaccinated patients. COVID-19 severity has been correlated with older age, male sex, and the presence of comorbidities; it is hypothesized that SARS-CoV-2 antibody responses are also correlated.159 unvaccinated patients with SARS-CoV-2 infections were assessed for IgA, IgG, and IgM titers, which were compared with disease severity, age, sex, presence of comorbidities, and time since infection. Anti-S and anti-Nucleocapsid (N) IgG responses were compared between unvaccinated and vaccinated SARS-CoV-2 positive patients. Anti-S IgA and IgM were better indicators of disease severity than IgG. IgG responses were more likely for patients over 60 years old. Female patients over 60 were more likely to have an antibody response than female patients under 60. Vaccinated patients had a stronger IgG response against S protein than against N protein likely due to immune imprinting. Disease severity was correlated with anti-S antibody responses and comorbidities in unvaccinated patients.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".