Acute right-sided ischemic colitis in a COVID-19 patient: a case report and review of the literature
Bibliographic record
Abstract
INTRODUCTION: In addition to attacking the respiratory system, the coronavirus disease may attack the gastrointestinal tract in various ways, one of which is by creating a coagulopathy that may lead to acute ischemia of the bowel, increasing morbidity and mortality rates in these patients. PRESENTATION OF CASE: We present a case of a white 72-year-old European male, who was admitted to the intensive care unit after developing COVID-19-induced acute respiratory distress syndrome. On the third week, despite a favorable evolution of his respiratory symptoms, the patient became clinically septic; laboratory findings showed an augmentation of his D-dimer, fibrinogen, C-reactive protein, and procalcitonin levels. Imaging showed signs of ischemia of the right colon. The patient was taken to the operating room; only the right side of his colon was ischemic, with a well demarcated cut-off. A laparoscopic right hemicolectomy with a terminal ileostomy was performed. The patient was able to go home 2 weeks after surgery. DISCUSSION AND CONCLUSION: Ischemic colitis is an uncommon pathology in the general population, and is rare in COVID-19 patients. Most cases of ischemic colitis in COVID-19 patients in the literature were limited to the left colon, with < 10 cases involving the right colon. Accurate and quick diagnosis with appropriate management is the key to avoid any mortality in those patients who are already weakened by the coronavirus.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".