COVID-19 and Gastrointestinal Disease: Implications for the Gastroenterologist
Bibliographic record
Abstract
BACKGROUND: COVID-19 was initially considered a respiratory disease but the SARS-CoV-2 virus can lead to serious systemic consequences affecting major organs including the digestive system. SUMMARY: This review brings new clinically important information for the gastroenterologist. This includes: the mechanisms of tissue damage seen with the SARS-CoV-2 virus; the consequences of immunosuppression in patients with inflammatory bowel disease (IBD) and chronic liver disease with the additional risks of decompensation in patients with cirrhosis; the impact of COVID-19 on gastrointestinal emergencies, on gastrointestinal endoscopy, diagnosis and treatments. These highlight the need to understand the clinical pharmacology, toxicology and therapeutic implications of drugs commonly used by gastroenterologists and their links with COVID-19. Key Messages: Any part of the digestive system may be affected by the SARS-CoV-2 virus, and those with pre-existing disease are at greatest risk of adverse outcomes. The risk for drug-drug interactions is considerable in patients seriously ill with COVID-19 who often require mechanical ventilation and life support. Some repurposed drugs used against SARS-CoV-2 can cause or aggravate some of the COVID-19-related gastrointestinal symptoms and can also induce liver injury. Ongoing clinical studies will hopefully identify effective drugs with a more favourable risk-benefit ratio than many initially tried treatments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".