Designing, Developing, and Validating a Set of Standardized Pictograms to Support Pediatric-Reported Gastroduodenal Symptoms
Bibliographic record
Abstract
Objective To develop and validate a set of static and animated gastroduodenal symptom pictograms for children. Study design There were three study phases: 1: co-creation using experience design methods to develop pediatric gastroduodenal symptom pictograms (static and animated); 2: an online survey to assess acceptability, as well as face and content validity; and 3: a preference study. Phases 2 and 3 compared the novel pediatric pictograms with existing pictograms used with adult patients. Results Eight children aged 6-15 years (5 female) participated in phase 1, and 69 children in phase 2 (median age 13 years: IQR 9-15); an additional 49 participants were included in phase 3 (median age 15: IQR 12-17). Face and content validity were higher for the pediatric static and animated pictogram sets compared with pre-existing adult pictograms (78% vs. 78% vs. 61%). Participants with worse gastric symptoms had superior comprehension of the pediatric pictograms (χ 2 (8, N=118) p< .001). All participants preferred the pediatric static pictogram set was over both the animated and adult sets (χ 2 (2, N=118) p< .001). Conclusions 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.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".