Gastroenterological and Hepatic Manifestations of Patients with COVID-19, Prevalence, Mortality by Country, and Intensive Care Admission Rate: Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background & Aims Patient infected with the SARS-COV2 usually report fever and respiratory symptoms. However, multiple gastrointestinal (GI) manifestations such as diarrhea and abdominal pain has been described. The aim of this study was to evaluate the prevalence of GI, liver function test (LFT) abnormalities, and mortality of COVID-19 patients. Methods We performed a systematic review and meta-analysis of published studies that included cohort of patients infected with SARS-COV2 from December 1 st , 2019 to July 1 st , 2020. We collected data from the cohort of patients with COVID-19 by conducting a literature search using PubMed, Embase, Scopus, and Cochrane according to the preferred reporting Items for Systematic Reviews and MetaAnalyses (PRISMA) guidelines. We analyzed pooled data on the prevalence of overall and individual gastrointestinal symptoms, LFTs abnormalities and performed subanalyses to investigate the relationship between gastrointestinal symptoms, geographic location, fatality, and ICU admission. Results The available data of 17,802 positive patients for SARS-COV2 from 120 studies were included in our analysis. The most frequent manifestations were diarrhea (13.3%, 95% CI 12-16), nausea (9.1%, 95% CI 9-13) and elevated LFTs (23.7%, 95% CI 21- 27). The overall and GI fatality were 7.2% (95% CI 6 -10), and 1% (95% CI 1- 4) respectively. Subgroup analysis showed non statistically significant associations between GI symptoms/LFTs abnormalities and ICU admissions (OR=3.41, 95% CI 0.87 – 13.4). The GI mortality rate was 0.58% in China and 3.5% in the United States (95% CI 2 - 5). Conclusion Digestive symptoms and LFTs abnormalities are common in COVID-19 patients. Our subanalysis shows that the presence of gastrointestinal and liver manifestations does not appear to affect mortality, or ICU admission rate. However, the mortality rate was higher in the United States compared to China.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".