Pain is a cardinal symptom cutting across Rome <scp>IV</scp> anatomical categories in disorders of gut‐brain interaction: A network‐based approach
Bibliographic record
Abstract
INTRODUCTION: Disorders of gut-brain interaction (DGBI) are symptom-based disorders categorized by anatomic location but have high overlap and heterogeneity. Viewing DGBI symptoms on a spectrum (i.e. dimensionally) rather than categorically may better inform interventions to accommodate complex clinical presentations. We aimed to evaluate symptom networks to identify how DGBI symptoms interact. METHODS: We used the Rome IV Diagnostic Questionnaire continuously/ordinally scored items collected from the Rome Foundation Global Epidemiology Study. We excluded participants who reported ≥1 organic/structural gastrointestinal disorder(s). We sought to (1) identify core symptoms in the DGBI symptom networks, (2) identify bridge pathways between Rome IV diagnostic categories (esophageal, bowel, gastroduodenal, anorectal), and (3) explore how symptoms group together into communities. RESULTS: Of 54,127 adults, 20,229 met criteria for at least one DGBI (age mean = 42.2 ± 15.5; 57% female). General abdominal pain and epigastric pain were the core symptoms in the DGBI symptom network (i.e., had the strongest connections to other symptoms). Pain symptoms emerged as bridge pathways across existing DGBI diagnostic anatomic location (i.e., abdominal pain connected to chest pain, epigastric pain, rectal pain). Without a priori category definitions, exploratory network community analysis showed that symptoms grouped together into "pain," "gastroduodenal," and "constipation," rather than into groups by anatomic location. CONCLUSION: Our findings suggest pain symptoms are central and serve as a key connection to other symptoms, crosscutting anatomic location. Future longitudinal research is needed to test symptom network relations longitudinally and investigate whether targeting pain symptoms (rather than anatomic- or disorder-specific symptoms) has clinical impact.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".