The Inflammatory Bowel Disease Symptom Inventory: A Patient-report Scale for Research and Clinical Application
Bibliographic record
Abstract
OBJECTIVES: Existing measures of inflammatory bowel disease (IBD) symptoms are not well suited to self-report, inadequate in measurement properties, insufficiently specific, or burdensome for brief or repeated administration. We aimed to develop a patient-reported outcome measure to assess a broader range of IBD symptoms. METHODS: The IBD Symptoms Inventory (IBDSI) was developed by adapting symptom items from existing clinician-rated or diary-format inventories; after factor analysis, 38 items were retained on 5 subscales: bowel symptoms, abdominal discomfort, fatigue, bowel complications, and systemic complications. Participants completed the IBDSI and other self-report measures during a clinic visit. A nurse administered the Harvey Bradshaw Index (HBI) for Crohn's disease (CD) or the Powell-Tuck Index (PTI) for ulcerative colitis (UC), and a gastroenterologist completed a global assessment of disease severity (PGA). RESULTS: The 267 participants with CD (n = 142) or UC (n = 125), ages 18 to 81 (M = 43.4, SD = 14.6) were 58.1% female, with a mean disease duration of 13.9 (SD = 10.5) years. Confirmatory factor analysis supported the 5 subscales. The total scale and subscales showed good reliability and significant correlations with self-report symptom and IBD quality of life measures, the HBI, PTI, and PGA. CONCLUSIONS: The IBDSI showed strong measurement properties: a supported factor structure, very good internal consistency, convergent validity, and excellent sensitivity and specificity to clinician-rated active disease. Self-report HBI and PTI items, when extracted from this measure, produced scores comparable to clinician-administered versions. The 38-item IBDSI, or 26-item short form, can be used as a brief survey of common IBD symptoms in clinic or research settings.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".