Global patterns of prescription pain medication usage in disorders of <scp>gut–brain</scp> interactions
Bibliographic record
Abstract
BACKGROUND: Forty percent of individuals globally meet Rome IV criteria for a disorder of gut-brain interaction (DGBI). The global burden of pain across these disorders has not been characterized. METHODS: Our study included 54,127 respondents from the 26 Internet survey countries. Prescription pain medication usage was selected as the proxy for pain. The associations between prescription pain medications and the environmental, sociodemographic, psychosocial, and DGBI diagnosis variables were investigated using the multivariate generalized robust Poisson regression model. KEY RESULTS: Respondents with DGBI used prescription pain medications at higher rates than those without a DGBI diagnosis with pooled prevalence rate of 14.8% (95% confidence interval [CI], 14.4-15.3%), varying by country from 6.8% to 25.7%. The pooled prevalence ratio of prescription pain medication usage in respondents with and without DGBI was 2.2 (95% CI: 2.1-2.4). Factors associated with higher prevalence of pain medication usage among respondents with a DGBI diagnosis included living in a small community, increased anxiety, depression or somatization, increased stress concern or embarrassment about bowel functioning and having more than one anatomic DGBI diagnosis. CONCLUSION: 14.8% of patients globally with at least one diagnosis of DGBI were on prescription pain medications with wide geographic variation, about twice as many as their counterparts without a diagnosis of DGBI. Environmental, sociodemographic, and individual factors may influence clinicians to consider personalized, multimodal approaches to address pain in patients with DGBI.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".