Diagnostic classification systems for disorders of gut‐brain interaction should include psychological symptoms
Bibliographic record
Abstract
BACKGROUND AND AIMS: The group of disorders known as Disorders of Gut Brain Interaction (DGBI) were originally labeled functional GI disorders and were thought to be disorders of the gastrointestinal tract that had several psychological conditions as comorbidities. Despite mounting evidence that psychological morbidity plays an innate role in the etiology and maintenance of DGBI, none of the Rome IV criteria include any measure of psychological symptoms. This study tested the hypothesis that individuals would cluster differently if GI symptoms alone were considered versus GI symptoms combined with measures of psychological symptoms. METHODS: Data were obtained from the Rome Foundation Global Epidemiology Study measuring Rome IV GI symptoms, psychological measures and demographic characteristics. Latent profile models were used to cluster individuals based on (i) GI symptoms only (GI only) and then (ii) GI and psychological measures (GI + Psych). KEY RESULTS: Individuals clustering into the same group of individuals whether formed via GI only or GI + Psych, ranged from 96% for a 2-class solution (the most simplistic) to 76% with 6 classes (the parsimonious system) and 59% with twenty-two classes (mimicking Rome IV). The generalisability of this finding between six geographic regions was confirmed with agreement varying between 95%-97% for 2 clusters and 71-79% for 6 classes and 51%-63% for 22 classes. These findings were also consistent between DGBI (range 94% with 2 classes to 50% with 22 classes) and non-DGBI (range 97% with 2 clusters to 65% with 22 classes) groups. CONCLUSIONS & INFERENCES: Our data suggest that considering psychological as well as gastrointestinal symptoms would lead to a different clustering of individuals in more complex, and accurate, classification systems. For this reason, future work on DGBI classification should consider inclusion of psychological traits.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".