Microbiota signatures and mucosal healing in the use of enteral nutrition therapy <i>v</i>. corticosteroids for the treatment of children with Crohn’s disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Corticosteroids (CS) and exclusive and partial enteral nutrition (EEN and PEN) are effective therapies in paediatric Crohn’s disease (CD). This systematic review of randomised controlled trials (RCT) and cohort studies analyses the impact of EEN/PEN v . CS on intestinal microbiota, mucosal healing as well as other clinically important outcomes, including clinical remission, relapse, adherence, adverse events and health-related quality of life (HRQL) in paediatric CD. Three RCT ( n 76) and sixteen cohort studies ( n 1104) compared EEN v . CS. With limited available data (one RCT), the effect on intestinal microbiome indicated a trend towards EEN regarding Shannon diversity. Based on two RCT, EEN achieved higher mucosal healing than CS (risk ratio (RR) 2·36, 95 % CI (1·22, 4·57), low certainty). Compared with CS, patients on EEN were less likely to experience adverse events based on two RCT (RR 0·32, 95 % CI (0·13, 0·80), low certainty). For HRQL, there was a trend in favour of CS based on data from two published abstracts of cohort studies. Based on thirteen cohort studies, EEN achieved higher clinical remission than CS (RR 1·18, 95 % CI (1·02, 1·38), very low certainty). Studies also reported no important differences in relapse and adherence. Compared with CS, EEN may improve mucosal healing with fewer adverse events based on RCT data. While limited data indicate the need for further trials, this is the first systematic review to comprehensively summarise the data on intestinal microbiome, mucosal healing and HRQOL when comparing enteral nutrition and CS in paediatric CD.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.029 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".