Systematic review and meta-analysis of lactose digestion, its impact on intolerance and nutritional effects of dairy food restriction in inflammatory bowel diseases
Bibliographic record
Abstract
BACKGROUND: Relationships between inflammatory bowel disease and lactose containing foods remain controversial and poorly defined regarding symptoms, nutritional outcomes, and epidemiologic associations for lactose maldigestion. METHODS: A literature review was performed using Pub Med, Cochrane library and individual references, to extract data on lactose maldigestion prevalence in inflammatory bowel diseases. A meta-analysis was done using selected articles, to determine odds ratios of maldigestion. Information was collected about symptoms, impact on pattern of dairy food consumption, as well as the effects of dairy foods on the course of inflammatory bowel diseases. RESULTS: A total of 1022 articles were evaluated, 35 articles were retained and 5 studies were added from review articles. Of these 17 were included in meta-analysis which showed overall increased lactose maldigestion in both diseases. However increased risk on sub analysis was only found in Crohn's in patients with small bowel involvement. Nine additional studies were reviewed for symptoms, with variable outcomes due to confounding between lactose intolerance and lactose maldigestion. Fourteen studies were evaluated for dairy food effects. There was a suggestion that dairy foods may protect against inflammatory bowel disease. Nutritional consequences of dairy restrictions might impact adversely on bone and colonic complications. CONCLUSIONS: Lactose maldigestion in inflammatory bowel disease is dependent on ethnic makeup of the population and usually not disease. No bias of increased disease prevalence was noted between lactase genotypes. Intolerance symptoms depend on several parameters besides lactose maldigestion. Dairy foods may decrease risks of inflammatory bowel disease. Dairy restrictions may adversely affect disease outcome.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.039 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".