A new clinical severity score for the management of acute small bowel obstruction in predicting bowel ischemia: a cohort study
Bibliographic record
Abstract
BACKGROUND: Small bowel obstruction (SBO) is a common hospital admission diagnosis. Identification of patients who will require a surgical resection because of a nonviable small bowel remains a challenge. Through a prospective cohort study, the authors aimed to validate risk factors and scores for intestinal resection, and to develop a practical clinical score designed to guide surgical versus conservative management. PATIENTS AND METHODS: All patients admitted for an acute SBO between 2004 and 2016 in the center were included. Patients were divided in three categories depending on the management: conservative, surgical with bowel resection, and surgical without bowel resection. The outcome variable was small bowel necrosis. Logistic regression models were used to identify the best predictors. RESULTS: Seven hundred and thirteen patients were included in this study, 492 in the development cohort and 221 in the validation cohort. Sixty-seven percent had surgery, of which 21% had small bowel resection. Thirty-three percent were treated conservatively. Eight variables were identified with a strong association with small bowel resection: age 70 years of age and above, first episode of SBO, no bowel movement for greater than or equal to 3 days, abdominal guarding, C-reactive protein greater than or equal to 50, and three abdominal computer tomography scanner signs: small bowel transition point, lack of small bowel contrast enhancement, and the presence of greater than 500 ml of intra-abdominal fluid. Sensitivity and specificity of this score were 65 and 88%, respectively, and the area under the curve was 0.84 (95% CI: 0.80-0.89). CONCLUSION: The authors developed and validated a practical clinical severity score designed to tailor management of patients presenting with an SBO.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".