Antibody response to SARS-CoV-2 among individuals with IBD diminishes over time: a serosurveillance cohort study
Bibliographic record
Abstract
We read with interest the highly impactful CLARITY IBD study that demonstrated a diminished serological response to SARS-CoV-2 in individuals with IBD receiving infliximab as compared with those receiving vedolizumab.1 We assessed the prevalence of antibodies to SARS-CoV-2 in patients with IBD and evaluated whether serological response to SARS-CoV-2 wanes over time following infection. Individuals with IBD from Calgary, Canada, were recruited from October 2020 to April 2021 and stratified into two cohorts: (1) serosurveillance (n=279) defined as those who were not previously diagnosed with COVID-19; and (2) COVID-19 recovered (n=45), defined as those with a molecular-confirmed diagnosis of SARS-CoV-2 infection via PCR. Serum was analysed using the Abbott Architect SARS-CoV-2 IgG antibody to the nucleocapsid protein. A positive antibody test was defined as an index signal/cut-off ≥1.4. The assay has been validated with 95% sensitivity and >99% specificity 12 days following symptom onset and correlates to neutralising antibodies.2 We assessed the proportion, with 95% CIs, of individuals with IBD who mounted an antibody response among those undergoing serosurveillance and following infection with SARS-CoV-2. In those diagnosed with COVID-19, we calculated the days from diagnosis to serological testing and developed a logistic regression model that assessed the effect of time from COVID-19 diagnosis to serology testing (before vs after 90 days) on nucleocapsid positive versus negative antibody; adjusted for age, sex and anti-tumour necrosis factor (TNF) usage. The baseline characteristics of the two cohorts are summarised in table 1. Among the serosurveillance cohort, 1.08% (95% CI 0.22 to …
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".