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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".