Diet and Inflammatory Bowel Disease: What Quality Standards Should Be Applied in Clinical and Laboratory Studies?
Bibliographic record
Abstract
Many patients suffering from inflammatory bowel disease (IBD) follow restrictive diets, as many respective recommendations circulate. Efforts are made to evaluate and summarize the published information, for example, in a recent consensus manuscript by the International Organization for the Study of IBD (IOIBD). However, the standards that should be applied to make claims about dietary effects are poorly defined. In this manuscript, the scientific basis of recommendations for nutritional interventions in IBD is analyzed. Epidemiological evidence on diet in IBD is always biased by numerous factors, and the number of robust dietary intervention studies is limited due to methodological difficulties. Therefore, animal models are used to test hypotheses with respect to dietary factors and intestinal inflammation. Naturally, animal models have limitations, and knowledge of key characteristics of colitis animal models is crucial to understand their advantages and disadvantages. In recent years the important role of the microbiota for IBD and dietary factors has been discovered. Microbiota data are added to many publications on IBD and nutrition. The quality of those data varies largely. Subsequently, quality standards for microbiota analyses also are discussed. Finally, quality requirements to be applied on recommendations for dietary changes in patients with IBD are suggested.
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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 | 0.105 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".