727. Outcomes of Antibiotic Use in Ischemic Colitis
Bibliographic record
Abstract
Abstract Background Ischemic colitis (IC) is caused by inadequate blood flow to the colon. Most cases resolve with conservative management. Isolated right-sided colitis, peritonitis, shock, and vascular risk factors are predictors of severe disease which can be life-threatening and require surgery. Current guidelines recommend antibiotics for moderate/severe disease. This is based on results from animal models and concern for gut translocation of bacteria; there have been no comparative studies in humans. This study aims to evaluate whether there is benefit to antibiotic use in non-severe IC. Methods This is a single-center retrospective cohort study of adult patients hospitalized with IC from 2015-2018. Inclusion in the study required endoscopic, radiologic, operative, or histologic evidence of ischemic colitis. Patients were divided into mild/moderate and severe IC cohorts as per 2014 American College of Gastroenterology Guidelines. Primary outcomes were length of stay (LOS) and any adverse event, which is defined as a composite measure of pre-specified secondary outcomes including mortality, need for surgery, 1-year relapse, and bacteremia. Results Of 191 patients enrolled in the study, 130 had mild/moderate IC and 61 had severe IC. In mild/moderate IC groups there was no significant difference in total adverse events, although use of antibiotics was associated with a significant increase in LOS (Table 1). In the severe IC groups there was no significant difference in any primary outcomes, but mortality was lower at 3 and 6 months among patients who did not receive antibiotics. Table 1 Conclusion Antibiotics did not improve outcomes in mild/moderate IC, suggesting that conservative management may be sufficient in this group. Antibiotic use was associated with increased LOS in mild/moderate IC and with increased mortality in severe IC; it is not clear whether these associations are true antibiotic-mediated adverse effects or whether they simply reflect a tendency to use antibiotics more frequently in patients who are more unstable. Future prospective research is needed to establish clear guidelines for antibiotic indications, agent selection, and optimal treatment duration. Disclosures All Authors: No reported disclosures
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".