Pictograms to assess bloating and distension symptoms in the general population in Mexico: Results of The Rome Foundation Global Epidemiology Study
Bibliographic record
Abstract
BACKGROUND: There is no term for bloating in Spanish and distension is a very technical word. "Inflammation"/"swelling" are the most frequently used expressions for bloating/distension in Mexico, and pictograms are more effective than verbal descriptors (VDs) for bloating/distension in general GI and Rome III-IBS patients. However, their effectiveness in the general population and in subjects with Rome IV-DGBI is unknown. We analyzed the use of pictograms for assessing bloating/distension in the general population in Mexico. METHODS: The Rome Foundation Global Epidemiology Study (RFGES) in Mexico (n = 2001) included questions about the presence of VDs "inflammation"/"swelling" and abdominal distension, their comprehension, and pictograms (normal, bloating, distension, both). We compared the pictograms with the Rome IV question about the frequency of experiencing bloating/distension, and with the VDs. KEY RESULTS: "Inflammation"/"swelling" was reported by 51.5% and distension by 23.8% of the entire study population; while 1.2% and 25.3% did not comprehend "Inflammation"/"swelling" or distension, respectively. Subjects without (31.8%) or not comprehending "inflammation"/"swelling"/distension (68.4%) reported bloating/distension by pictograms. Bloating and/or distension by the pictograms were much more frequent in those with DGBI: 38.3% (95%CI: 31.7-44.9) vs. without: 14.5% (12.0-17.0); and in subjects with distension by VDs: 29.4% (25.4-33.3) vs. without: 17.2% (14.9-19.5). Among subjects with bowel disorders, those with IBS reported bloating/distension by pictograms the most (93.8%) and those with functional diarrhea the least (71.4%). CONCLUSIONS & INFERENCES: Pictograms are more effective than VDs for assessing the presence of bloating/distension in Spanish Mexico. Therefore, they should be used to study these symptoms in epidemiological research.
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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 | 0.001 |
| 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".