Pictograms to assess bloating and distension symptoms in the general population in Mexico: Results of The Rome Foundation Global Epidemiology Study
Bibliographic record
Abstract
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.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."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%).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 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".