Lower incidence of COVID‐19 in patients with inflammatory bowel disease treated with non‐gut selective biologic therapy
Bibliographic record
Abstract
BACKGROUND AND AIM: Since the outbreak of COVID-19, concerns have been raised as to whether inflammatory bowel disease (IBD) patients under biologic therapy may be more susceptible to the disease. This study aimed to determine the incidence and outcomes of COVID-19 in a large cohort of IBD patients on biologic therapy. METHODS: This observational retrospective multicenter study collected data about COVID-19 in IBD patients on biologic therapy in Italy, between February and May 2020. The main end-points were (i) to assess both the cumulative incidence and clinical outcome of COVID-19, according to different biologic agents and (ii) to compare them with the general population and a cohort IBD patients undergoing non-biologic therapies. RESULTS: Among 1816 IBD patients, the cumulative incidence of COVID-19 was 3.9 per 1000 (7/1816) with a 57% hospitalization rate and a 29% case-fatality rate. The class of biologic agents was the only risk factor of developing COVID-19 (P = 0.01). Non-gut selective agents were associated with a lower incidence of COVID-19 cases, related symptoms, and hospitalization (P < 0.05). Compared with the general population of Lombardy, an overall lower incidence of COVID-19 was observed (3.9 vs 8.5 per 1000, P = 0.03). Compared with 565 IBD patients on non-biologic therapies, a lower rate of COVID-19 symptoms was observed in our cohort (7.5% vs 18%, P < 0.001). CONCLUSIONS: Compared with the general population, IBD patients on biologic therapy are not exposed to a higher risk of COVID-19. Non-gut selective agents are associated with a lower incidence of symptomatic disease, supporting the decision of maintaining the ongoing treatment.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".