The SIBDQ: further validation in ulcerative colitis patients
Bibliographic record
Abstract
OBJECTIVE: The Inflammatory Bowel Disease Questionnaire (IBDQ) is an instrument that assesses quality of life in patients with inflammatory bowel disease. It has 32 items in four domains. The short form of the IBDQ (SIBDQ) was developed in Canadian Crohn's disease patients for use in clinical practice. Patients with ulcerative colitis might require a different form of the SIBDQ. Our aim was to design and validate a SIBDQ for patients with ulcerative colitis and to compare this to the Crohn's SIBDQ. METHODS: We recruited 122 patients with colitis as an initial sample. Using linear regression modeling, the 10 items that best predicted the total IBDQ score were identified. The colitis and Crohn's versions of the SIBDQ were compared by univariate linear regression with the total IBDQ score in two other cohorts of colitis patients. RESULTS: Ten items explained 97% of the variance of the total IBDQ score in our first cohort. These were items 1 and 9 (bowel); 7, 11, 21, 30 (emotional); 2 and 10 (systemic); and 12 and 28 (social). Only three items were shared with the Crohn's SIBDQ. The R2 for both SIBDQs with the total IBDQ score in the other cohorts were very high (> or =0.95), although the Colitis SIBDQ showed better internal consistency. CONCLUSIONS: The development of a SIBDQ for patients with ulcerative colitis did not reveal any clear advantage over the original version of the SIBDQ. Further studies are required to determine the role of the SIBDQ in routine clinical practice.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".