Gastrointestinal and hepatic side effects of potential treatment for COVID-19 and vaccination in patients with chronic liver diseases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The outbreak of coronavirus disease 2019 (COVID-19) is a global pandemic. Many clinical trials have been performed to investigate potential treatments or vaccines for this disease to reduce the high morbidity and mortality. The drugs of higher interest include umifenovir, bromhexine, remdesivir, lopinavir/ritonavir, steroid, tocilizumab, interferon alpha or beta, ribavirin, fivapiravir, nitazoxanide, ivermectin, molnupiravir, hydroxychloroquine/chloroquine alone or in combination with azithromycin, and baricitinib. Gastrointestinal (GI) symptoms and liver dysfunction are frequently seen in patients with COVID-19, which can make it difficult to differentiate disease manifestations from treatment adverse effects. GI symptoms of COVID-19 include anorexia, dyspepsia, nausea, vomiting, diarrhea and abdominal pain. Liver injury can be a result of systemic inflammation or cytokine storm, or due to the adverse drug effects in patients who have been receiving different treatments. Regular monitoring of liver function should be performed. COVID-19 vaccines have been rapidly developed with different technologies including mRNA, viral vectors, inactivated viruses, recombinant DNA, protein subunits and live attenuated viruses. Patients with chronic liver disease or inflammatory bowel disease and liver transplant recipients are encouraged to receive vaccination as the benefits outweigh the risks. Vaccination against COVID-19 is also recommended to family members and healthcare professionals caring for these patients to reduce exposure to the severe acute respiratory syndrome coronavirus 2 virus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it