Crohn’s and Colitis Canada’s 2021 Impact of COVID-19 and Inflammatory Bowel Disease in Canada: Seniors With IBD
Bibliographic record
Abstract
The risk of hospitalization and death from Coronavirus disease-19 (COVID-19) increases with age. The extreme elderly have been particularly vulnerable, with those above the age of 80 having a case-fatality rate as high as 15%. Aging of the immune system can lead to impaired inflammatory responses where eradication of an organism such as Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV2) is inadequate but is exaggerated in such a way as to enhance pneumonia and acute respiratory distress syndrome. Frailty and comorbidity are both more common in the elderly, and these can enhance the morbidity and mortality from COVID-19. Studies from Northern California and Italy suggest that elderly persons with inflammatory bowel disease (IBD) were more likely to acquire SARS-CoV-2 infection than youths with IBD. While the specific impact of age-related comorbidity is less well established among people with IBD who acquire COVID-19, data from the Surveillance Epidemiology of Coronavirus Under Research Exclusion (SECURE-IBD) database reported that having two or more chronic illnesses was independently associated with developing severe COVID-19 among people with IBD. Despite having exaggerated auto-inflammatory responses, people with IBD do not appear to have an overall increased risk of developing severe COVID-19 than the general population. However, whether seniors with IBD do worse once they acquire COVID-19 compared with seniors without IBD is not known. The advent of telehealth care has posed an information technology challenge for many seniors with and without IBD. Most persons with IBD have expressed satisfaction with virtual IBD health care (phone or video-based visits). While the elderly may have less robust immune responses to vaccinations, learning from experiences with other vaccination programs, especially influenza, have shown that vaccinating seniors decreases both morbidity and mortality and, in turn, healthcare resources.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".