Crohn’s and Colitis Canada’s 2021 Impact of COVID-19 and Inflammatory Bowel Disease in Canada: COVID-19 Vaccines—Biology, Current Evidence and Recommendations
Bibliographic record
Abstract
The COVID-19 pandemic has ushered a globally focused vaccine development program that produced multiple successful vaccines within a year. Four SARS-CoV-2 vaccines have been approved for use in Canada, using two different technologies, all of which have shown excellent efficacy in reducing the rate of symptomatic COVID-19 infection and 100% efficacy in preventing death from COVID-19. People with inflammatory bowel disease (IBD), like many others with immune-mediated chronic diseases, were excluded from the pivotal trials of these vaccines, leading to early hesitancy by regulatory bodies to endorse administering the vaccines to these groups. However, recent data has shown that the adverse event rate to SARS-CoV-2 vaccine among people with IBD is similar to the general population. Early data has further shown that people with IBD are capable of mounting a robust immune response to SARS-CoV-2 vaccines, particularly following a second dose, whereas the response to the first dose is blunted in those receiving anti-TNF therapy or conventional immunosuppressants (azathioprine, 6-mercaptopurine, methotrexate). Based on these data and evidence from previous vaccine programs among people with IBD, multiple national and international expert panels have recommended that individuals with IBD receive complete vaccination against SARS-CoV-2 as soon as possible.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".