Symptom profiles compatible with disorders of gut‐brain interaction (DGBI) in organic gastrointestinal diseases: A global population‐based study
Bibliographic record
Abstract
BACKGROUND: Patients with organic gastrointestinal (GI) diseases and diabetes mellitus (DM) can have concomitant disorders of gut-brain interaction (DGBI). OBJECTIVE: This study aimed to compare the global prevalence of DGBI-compatible symptom profiles in adults with and without self-reported organic GI diseases or DM. METHODS: Data were collected in a population-based internet survey in 26 countries, the Rome Foundation Global Epidemiology Study (n = 54,127). Individuals were asked if they had been diagnosed by a doctor with gastroesophageal reflux disease, peptic ulcer, coeliac disease, inflammatory bowel disease (IBD), diverticulitis, GI cancer or DM. Individuals not reporting the organic diagnosis of interest were included in the reference group. DGBI-compatible symptom profiles were based on Rome IV diagnostic questions. Odds ratios (ORs [95% confidence interval]) were calculated using mixed logistic regression models. RESULTS: Having one of the investigated organic GI diseases was linked to having any DGBI-compatible symptom profile ranging from OR 1.64 [1.33, 2.02] in GI cancer to OR 3.22 [2.80, 3.69] in IBD. Those associations were stronger than for DM, OR 1.26 [1.18, 1.35]. Strong links between organic GI diseases and DGBI-compatible symptom profiles were seen for corresponding (e.g., IBD and bowel DGBI) and non-corresponding (e.g., IBD and esophageal DGBI) anatomical regions. The strongest link was seen between fecal incontinence and coeliac disease, OR 6.94 [4.95, 9.73]. After adjusting for confounding factors, associations diminished, but persisted. CONCLUSION: DGBI-compatible symptom profiles are more common in individuals with self-reported organic GI diseases and DM compared to the general population. The presence of these concomitant DGBIs should be considered in the management of organic (GI) diseases.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".