Bibliographic record
Abstract
PURPOSE OF REVIEW: Treatment algorithms for inflammatory bowel disease are changing rapidly. Increased and earlier use of immunomodulatory drugs and availability of biologic agents have reduced dependence on corticosteroids and made mucosal healing a realistic goal. It is timely to debate the role of enteral nutrition in this evolving therapeutic armamentarium for Crohn's disease, and to examine the mechanisms of its anti-inflammatory effects in light of current understanding of disease pathogenesis. RECENT FINDINGS: Clinical studies have suggested that response to enteral nutrition is associated with decreased mucosal inflammation in Crohn's disease, that isolated Crohn's colitis is less responsive and that exclusive enteral nutrition is required. Basic research has demonstrated that lipids in the intestinal lumen can alter signalling of the mucosal immune system by intestinal epithelial cells. Exclusive enteral nutrition is associated with alteration of enteric microflora. SUMMARY: Enteral nutrition is an efficacious treatment of active inflammation involving the ileum; recent-onset disease may be particularly responsive. The significance of effects on enteric flora deserves further exploration in view of the importance of microbes to disease pathogenesis.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".