The Multiple Waves of COVID-19 in Patients With Inflammatory Bowel Disease: A Temporal Trend Analysis
Bibliographic record
Abstract
BACKGROUND: Cases of coronavirus disease 2019 (COVID-19) have emerged in discrete waves. We explored temporal trends in the reporting of COVID-19 in inflammatory bowel disease (IBD) patients. METHODS: The Surveillance Epidemiology of Coronavirus Under Research Exclusion for Inflammatory Bowel Disease (SECURE-IBD) is an international registry of IBD patients diagnosed with COVID-19. The average percent changes (APCs) were calculated in weekly reported cases of COVID-19 during the periods of March 22 to September 12, September 13 to December 12, 2020, and December 13 to July 31, 2021. RESULTS: Across 73 countries, 6404 cases of COVID-19 were reported in IBD patients. COVID-19 reporting decreased globally by 4.2% per week (95% CI, -5.3% to -3.0%) from March 22 to September 12, 2020, then climbed by 10.2% per week (95% CI, 8.1%-12.3%) from September 13 to December 12, 2020, and then declined by 6.3% per week (95% CI, -7.8% to -4.7%). In the fall of 2020, weekly reporting climbed in North America (APC, 11.3%; 95% CI, 8.8-13.8) and Europe (APC, 17.7%; 95% CI, 12.1%-23.5%), whereas reporting was stable in Asia (APC, -8.1%; 95% CI, -15.6-0.1). From December 13, 2020, to July 31, 2021, reporting of COVID-19 in those with IBD declined in North America (APC, -8.5%; 95% CI, -10.2 to -6.7) and Europe (APC, -5.4%; 95% CI, -7.2 to -3.6) and was stable in Latin America (APC, -1.5%; 95% CI, -3.5% to 0.6%). CONCLUSIONS: Temporal trends in reporting of COVID-19 in those with IBD are consistent with the epidemiological patterns COVID-19 globally.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".