Factors Related to the Development of Small‐Bowel Bacterial Overgrowth in Pediatric Intestinal Failure: A Retrospective Cohort Study
Bibliographic record
Abstract
Abstract Background Small bowel bacterial overgrowth (SBBO) is a challenge in the management of pediatric intestinal failure (PIF). Our goal was to determine the proportion of patients treated for SBBO and factors related to its development. Methods We completed a retrospective analysis of PIF patients referred between 2008 and 2014. Data were collected on factors related to intestinal failure (IF) and SBBO. The cohort was stratified on the diagnosis of SBBO and refractory SBBO. Statistical testing completed using t ‐test, χ 2 test, and logistic regression. Results Thirty‐five of 102 patients developed SBBO (34%), and 16 (16%) had refractory SBBO. SBBO was more likely in gastroschisis (40.0% vs 19.4%, P = .025), a shorter residual small bowel (SB) (45.4% vs 66.5%, P = .004), and patients were less likely to wean from parenteral nutrition (PN) (51.4% vs 85.1%, P < .0001). Refractory SBBO patients were likely to have gastroschisis (50.0% vs 22.1%, P = .020) and a shorter residual SB and large bowel remaining (23.2% vs 65.9%, P < .0001 and 60.6% vs 79.4%, P = .03, respectively) and less likely to wean from PN (37.5% vs 80.2%, P = .001). Logistic regression demonstrated that longer SB residual was protective ( P = .001; odds ratio [OR], 0.95; 95% CI, 0.93–0.99), and short bowel syndrome (SBS) as a cause of IF was a risk factor ( P = .001; OR, 0.04; 95% CI, 0.01–0.27). Conclusion A longer SB remnant was protective against SBBO. Patients with SBBO were more likely to have PIF caused by SBS.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".