Item Generation and Reduction Toward Developing a Patient‐reported Outcome for Pediatric Ulcerative Colitis (TUMMY‐UC)
Bibliographic record
Abstract
BACKGROUND: The Pediatric Ulcerative Colitis Activity Index (PUCAI) is a noninvasive clinician-based index, which reflects disease severity in pediatric ulcerative colitis (UC) when no endoscopy is performed. Here, we aimed to explore signs and symptoms important to children with UC and their caregivers as the first stage of developing a patient-reported outcome (PRO) measure for pediatric UC (ie, the TUMMY-UC index) to supplement endoscopic assessment. METHODS: Concept elicitation qualitative interviews were performed with children who have UC and their caregivers in 6 centers. Items were rank-ordered by the interviewees according to the frequency of endorsement and importance, graded on a 1 to 5 scale. RESULTS: A total of 46 children (ages 12.5 ± 3.3 years, range 7-18, 48% boys, 83% with pancolitis, 24% with moderate-severe disease) and 33 caregivers were interviewed (ie, 79 interviews). The following items were identified by the children, in decreasing order of weights: abdominal pain (importance × frequency weight 3.9), rectal bleeding (3.6), stool frequency (3.0), stool consistency (3.0), general well-being/fatigue (2.9), urgency (1.9), and nocturnal stools (1.6). Two other items were scored lower: lack of appetite (1.1) and weight loss (0.6). Children 13 to 18 years comprehended adult vocabulary, children 8 to 12 years comprehended simple vocabulary, and younger children had poor understanding in completing the questions. CONCLUSIONS: In this first stage of the TUMMY-UC development, items were generated and ranked by input from patients. These items are now being explored for optimal vocabulary and response options. The TUMMY-UC will supplement the PUCAI in clinical trial outcome assessment.
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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 | 0.027 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".