DESIGNING, DEVELOPING AND VALIDATING A SET OF STANDARDIZED PEDIATRIC PICTOGRAMS TO SUPPORT PEDIATRIC-REPORTED GASTRODUODENAL SYMPTOMS
Bibliographic record
Abstract
ABSTRACT Objective To develop and validate a set of static and animated pediatric gastroduodenal symptom pictograms. Methods There were three study phases: 1: Co-creation used experience design methods resulting in ten pediatric gastroduodenal symptom pictograms (static and animation); 2: an online survey to assess acceptability, face and content validity; and 3: a preference study. Phases 2 and 3 compared the novel paediatric pictograms with existing pictograms used with adult patients. Results Eight children aged 6-15 years (5 Female) participated in Phase 1, 69 children in Phase 2 (median age 13 years: IQR 9-15), and an additional 49 participants were included in Phase 3 (median age 15: IQR 12-17). Face and content validity were higher for the pediatric and animated pictogram sets compared to pre-existing adult pictograms (78% vs. 78% vs. 61%). Participants with worse gastric symptoms (lower PedsQL-GIS score) had superior comprehension of the pediatric pictograms (χ 2 8 < .001). The pediatric pictogram set was preferred by all participants over animation and adult (χ 2 2 < .001). Conclusion The co-creation phase resulted in the symptom concept confirmation and design of ten acceptable static and animated gastroduodenal pictograms with high face and content validity when evaluated with children aged 6 to 18. Validity was superior when children reported more problematic symptoms. Therefore, these pictograms could be used in clinical and research practice to enable standardized symptom reporting for children with gastroduodenal disorders. Why is it important ▪ Diagnosis of gastroduodenal disorders of the gut-brain interaction (DGBI) in pediatrics is difficult as symptoms often overlap. ▪ Pediatric patients find identifying and distinguishing symptoms difficult. ▪ Validated gastroduodenal symptom pictograms have been found to help adults accurately report their symptoms and have been used effectively to standardize symptom monitoring, including continuous symptom reporting during investigations. ▪ There are no validated pediatric gastroduodenal symptom pictograms. What we did ▪ Co-created a set of ten pediatric gastroduodenal symptom pictograms. ▪ Undertook a face and content validity study to assess the novel pictograms with 118 pediatric participants with a median PedsQL-GIS score of 86.1 (IQR 68.1-90.0). The Outcome ▪ Designed a novel set of pictograms with face and content validity that were preferred over other sets, enabling acceptable, simple and validated pediatric patient reporting of their gastroduodenal symptoms.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".