Food Intolerances
Bibliographic record
Abstract
Food intolerances are estimated to affect up to 20% of the population but complete understanding of diagnosis and management is complicated, given presentation and non-immunological mechanisms associated vary greatly. This review aims to provide a scientific update on common food intolerances resulting in gastrointestinal and/or extra-intestinal symptoms. FODMAP sensitivity has strong evidence supporting its mechanisms of increased osmotic activity and fermentation with the resulting distention leading to symptoms in those with visceral hypersensitivity. For many of the other food intolerances reviewed including non-coeliac gluten/wheat sensitivity, food additives and bioactive food chemicals, the findings show that there is a shortage of reproducible well-designed double-blind, placebo-controlled studies, making understanding of the mechanisms, diagnosis and management difficult. Enzyme deficiencies have been proposed to result in other food sensitivities including low amine oxidase activity resulting in histamine intolerance and sucrase-isomaltase deficiency resulting in reduced tolerance to sugars and starch. Lack of reliable diagnostic biomarkers for all food intolerances result in an inability to target specific foods in the individual. As such, a trial-and-error approach is used, whereby suspected food constituents are reduced for a short-period and then re-challenged to assess response. Future studies should aim to identify biomarkers to predict response to dietary therapies.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".