Hydrogen and methane breath testing for abdominal bloating of small intestinal origin
Bibliographic record
Abstract
Abdominal bloating with or without excessive gas production and with or without abdominal distention is common, very bothersome for patients, and is poorly understood. Bloating can be associated with gastrointestinal disorders, nutrient malabsorption, and systemic illnesses. The origin of abdominal bloating is often unclear, though it is usually associated with food intake and bowel motility dysfunction. Often, food allergies, food intolerance, or food poisoning are considered by the patient or physician as potential causes for bloating. A common concern in the patient’s mind is that healthcare professionals will dismiss or misdiagnose their complaint. The limited understanding of the pathophysiology of bloating leads to current empirical management, and bloating is often characterized as a “functional” disorder. Bloating carries a heavy clinical, psychological, and economic burden. Proper diagnosis will provide the patient with peace of mind and lead to effective treatment. This review focuses on the mechanisms and management of abdominal bloating associated with different small intestinal disorders identified by non-invasive hydrogen and methane breath tests.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".