Development of the symptoms and impacts questionnaire for Crohn's disease and ulcerative colitis
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcome (PRO) measures historically used in inflammatory bowel disease have been considered inadequate to support future drug labelling claims by regulatory agencies. AIMS: To develop PRO tools for use in Crohn's disease (CD) and ulcerative colitis (UC) following guidance issued by the US FDA and the ISPOR (International Society for Pharmacoeconomics and Outcomes Research). METHODS: Concept elicitation and cognitive interviews were conducted in adult patients (≥18 years) across the United States and Canada. Semi-structured interview guides were used to collect data, and interview transcripts were coded and analysed. Concept elicitation results were considered alongside existing literature and clinical expert opinion to identify candidate PRO items. Cognitive interviews evaluated concept relevance, interpretability and structure, and facilitated instrument refinement. Concept elicitation participants, except those with an ostomy, underwent centrally read endoscopy to assess inflammatory status. RESULTS: In all, 54 participants (mean age: 46.2 years; 66.7% female) were included in the CD concept elicitation interviews. In total, 80 symptom concepts and 61 impact concepts were identified. After three waves of cognitive interviews, the 31-item Symptoms and Impacts Questionnaire for CD (SIQ-CD) was developed. In the UC concept elicitation phase, 53 participants were interviewed (mean age: 41.4 years; 49.1% female). In total, 79 symptoms concepts and 49 impact concepts were identified. Following two waves of cognitive interviews, the 29-item Symptoms and Impacts Questionnaire for UC (SIQ-UC) was developed. Both instruments include four symptom and six impact domains. CONCLUSIONS: We developed PROs to support CD and UC drug labelling claims. Psychometric validation studies to evaluate instrument reliability and responsiveness are ongoing.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".