Early Predictors of Enteral Autonomy in Pediatric Intestinal Failure Resulting From Short Bowel Syndrome: Development of a Disease Severity Scoring Tool
Bibliographic record
Abstract
INTRODUCTION: Patients with short bowel syndrome (SBS) are dependent on parenteral nutrition (PN) while their bowel attempts to compensate. Our objective was to create an SBS disease severity score to predict the probability of achieving enteral autonomy (EA). METHODS: A retrospective cohort study of children with SBS managed by our Intestinal Rehabilitation Program was completed. Data abstracted included demographic, bowel anatomy, and outcome variables including conjugated bilirubin (CB) and enteral nutrition (EN) tolerated 6 months postoperatively. Univariate analysis and Cox proportional hazards (CPH) model were performed. A score was created based on weighting of coefficients. An α-value of < 0.05 was considered significant. RESULTS: One hundred thirty-nine patients were analyzed (61% males). Ninety-five (68%) achieved EA. Patients possessing >50% residual small bowel (hazard ratio [HR] 2.68 [95% confidence interval {CI} 1.60-4.49], P < 0.001), ileocecal valve intact (HR 0.61 [95% CI 0.37-1.01], P < 0.055), and >50% enteral tolerance at 6 months (HR 5.70 [95% CI 2.77-11.74] P < 0.001) were positively associated with EA. CB >34 µmol/L (2 mg/dL) was negatively associated with EA (HR 0.42 [95% CI0.27-0.66], P < 0.001). A severity score was created by weighting CPH parameter estimates (small bowel length >50%, ileocecal valve intact, CB <34 µmol/L, and EN >50% for a maximum score of 8), and disease severity strata were developed (severe [25.7% EA], moderate [52.9% EA], and mild [97.1% EA]). CONCLUSION: We propose a pediatric SBS disease severity score that predicts probability of EA. The score allows prognostication of individual patients and could assist research by adjusting outcome reporting or stratifying recruitment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".