Acute Complicated Pancreatitis With COVID-19 Infection as a Potential Cause
Bibliographic record
Abstract
The pandemic coronavirus disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to a significant and dramatic worldwide increase in morbidity and mortality rates throughout the year 2020. Although most patients present with respiratory symptoms such as cough, fever and shortness of breath, gastrointestinal symptoms have increasingly been reported and recognized as a major element of patients' presentation. It was found that angiotensin-converting enzyme 2 receptors (the entry receptors for SARS-CoV-2) are expressed in the pancreas in addition to the lungs, kidneys and the rest of the gastrointestinal tract, which raises the hypothesis that COVID-19 can stand as an etiology for acute pancreatitis in certain circumstances. Herein, we present a case of a COVID-19 patient who presented to the emergency room with clinical, laboratory and radiological findings of acute pancreatitis several days after being diagnosed with COVID-19 infection. He was admitted and treated conservatively. Several weeks following the initial episode, the patient developed a huge pancreatic pseudocyst, which was treated successfully with endoscopic cyst-gastrostomy. To the best of our knowledge, following a meticulous review of the current English literature, this is the second case of pancreatic pseudocyst development following acute pancreatitis due to COVID-19 infection.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".