Incidence and risk factors for anastomotic bleeding in lower gastrointestinal surgery
Bibliographic record
Abstract
OBJECTIVE: Although major anastomotic bleeding after lower gastrointestinal surgery is considered rare, it can be life-threatening if not properly managed. The objective of this study was to assess the incidence of postoperative lower gastrointestinal intraluminal bleeding and to identify its potential risk factors. This retrospective cohort study used data from charts of 314 patients who underwent digestive surgery of the colon or small intestine. Details are reported for their sociodemographic data, surgical approach, comorbidities, timing and presentation of intraluminal bleeding events, bleeding diagnosis, treatment strategies, hospital length of stay, and clinical complications. RESULTS: A total of 7 patients (2.3%) experienced intraluminal bleeding in the postoperative period. The average length of hospital stay before discharge was 12 days (median = 13 days). Patients with intraluminal bleeding had a significantly higher percentage of coronary artery diseases compared to patients without intraluminal bleeding (P value = .04), as well as having a cancer diagnosis (P value = .02). The clinical complications that were more likely in patients with intraluminal bleeding included requiring blood transfusions (P value = .01), reduction in hemoglobin (P value = .001), cardiac ischemia (P value = .02), and atrial fibrillations (P value = .02).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".