Summary of Best Evidence for the Dietary Management in Patients with High-Output Ileostomy
Bibliographic record
Abstract
Objective: This study aims to systematically search, screen, evaluate, and summarize the best evidence related to the dietary management of patients with high-output ileostomy, providing an evidence-based reference for the dietary management tailored to this specific demographic. Methods: A comprehensive search was conducted across multiple databases, including BMJ best practice, Up to Date, Guidelines International Network, Medlive, National Institute for Health and Care Excellence, Scottish Intercollegiate Guidelines Network, Registered Nurses Association of Ontario, JBI, Cochrane Library, PubMed, CNKI, Wanfang, and VIP databases. Additional searches included websites of professional organizations such as the European Society for Clinical Nutrition and Metabolism, Chinese Society for Parenteral and Enteral Nutrition, American Society for Parenteral and Enteral Nutrition, World Council of Enterostomal Therapists, United Ostomy Associations of America, and International Ostomy Association. Two researchers independently evaluated the quality of the included literature and extracted and summarized the evidence. Results: Eighteen articles were included: 2 clinical decision documents, 5 guidelines, 3 expert consensus statements, 6 expert opinions, and 2 evidence summaries. Thirty pieces of evidence were synthesized, covering nutritional risk screening, dietary guidance, dietary behavior guidance, health education, and follow-up care. Conclusion: The summarized best evidence is scientific and comprehensive, offering an evidence-based guide for healthcare professionals managing the dietary needs of patients with high-output ileostomy.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".