The Impact of COVID-19 on Outcomes of Ischemic Colitis: A Nationwide Retrospective Analysis
Bibliographic record
Abstract
Background: Ischemic colitis is the most common presentation of mesenteric ischemia and is associated with significant morbidity and mortality. Coagulopathy has been associated with the development of ischemic colitis. Coronavirus disease 2019 (COVID-19) infection can lead to a variety of pathology and physiological derangements, including coagulopathy. Some case reports have described severe ischemic colitis in patients with COVID-19 infection. Our study aimed to elucidate the impact of COVID-19 infection on ischemic colitis outcomes. Methods: Patients with a diagnosis of ischemic colitis were identified using the 2020 Nationwide Inpatient Sample (NIS). Patients were stratified based on the presence of COVID-19 infection. Data were collected regarding mortality, shock, blood transfusion, length of stay, hospital charges, age, gender, race, primary insurance, median income, hospital region, hospital bed size, and comorbidities. The relationship between COVID-19 and outcomes was analyzed using multivariate regression analysis. Results: A total of 67,685 patients were included in the final analysis. COVID-19 was associated with an increased risk of in-hospital mortality (adjusted odds ratio (aOR): 4.006, P < 0.001), shock (aOR: 1.62, P = 0.002), and blood transfusion (aOR: 1.49, P = 0.007). COVID-19 was also associated with an increased length of stay (16.2 days vs. 8.7 days) and higher total hospital charges ($268,884.1 vs. $145,805.9). Conclusions: Among hospitalized patients with ischemic colitis, COVID-19 infection was associated with worse outcomes and higher resource utilization. Further studies are needed to investigate the mechanisms underlying this association.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".