P515 Manipulating the microbiome in paediatric acute severe colitis with a cocktail of antibiotics: A pilot randomised controlled trial
Bibliographic record
Abstract
A previous case series suggested a benefit of antibiotic-cocktail in steroid-refractory paediatric UC. In this pilot randomised investigator-blinded controlled trial we aimed to evaluate the effectiveness of wide-spectrum antibiotic regimens in acute severe colitis (ASC) in addition to standard intravenous corticosteroid (IVCS) therapy. Children 2–18 years with ASC (i.e. PUCAI ≥ 65) refractory to oral steroids were randomised into two arms: the first received antibiotics in addition to IVCS (amoxicillin, vancomycin, metronidazole, doxycyclin (or ciprofloxacin in those younger than 8 years of age)-AB+IVCS), while the other received only IVCS. Children with proctitis, infections, and those treated with antibiotics in the preceding 2 weeks were excluded. The primary outcome was total PUCAI score at Day 5 of treatment. Missing data for ITT analysis were imputed using the NRI method for categorical variables and LOCF for continuous variables. Thirty children were randomised and two were excluded (one positive for CMV and one salmonella): 16 in the AB+IVCS and 12 in the IVCS arms (mean age 14 ± 2.7 years, range 7–18, 15 (54%) males, 23 (82%) extensive colitis). Baseline variables were similar between groups (PUCAI 73.1 ± 6.6 vs. 75 ± 7.1, respectively). The mean Day 5 PUCAI was 25 ± 16.7 vs. 40.4 ± 20.4, respectively (p = 0.037). Total PUCAI score at Day 5 of therapy. Bearing in mind that the trial was not powered for this, there were no differences in the need for second-line therapy during the admission nor in the colectomy rate 1 year following admission (19% vs. 17%; p = 0.89). Median admission days (IQR) was statistically similar (7.5 (5–10) vs. 9 (5.5–13); p = 0.35). Microbiome analysis upon admission was available for 22 children of whom 8 (36%) had a predominant bacterium (>33% abundance). In this RCT, the first ever performed in children with ASC, antibiotic cocktail in addition to IVCS improved disease activity on Day 5. Further studies are needed to determine whether this is associated with improved long-term hard outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".