Effect of Suboptimal Disease Control on Patient Quality of Life: Real-World Data from the Observational IBD-PODCAST Canada Trial
Bibliographic record
Abstract
BACKGROUND: The real-world application of STRIDE-II treatment targets to identify whether disease control is optimal in Crohn's disease (CD) and ulcerative colitis (UC) is not well known. AIMS: This study aimed to estimate proportions of patients with suboptimally controlled CD and UC in real-world Canadian healthcare settings and the impact on quality of life (QoL). METHODS: The noninterventional, multicenter, observational IBD-PODCAST Canada study comprised a single study visit involving routine assessments, patient- and clinician-completed questionnaires, and a retrospective chart review. Primary outcomes were proportions of patients with STRIDE-II-based red flags indicative of suboptimal disease control and mean ± standard deviation Short Inflammatory Bowel Disease Questionnaire (SIBDQ) scores. Secondary outcomes included proportions of patients and clinicians subjectively reporting suboptimal control. RESULTS: Among 163 enrolled patients from 10 sites, 45/87 patients with CD (51.7%; 95% CI: 40.8%, 62.6%) and 33/76 patients with UC (43.3%; 95% CI: 32.1%, 55.3%) had suboptimal disease control based on STRIDE-II criteria. Suboptimal control was subjectively reported at lower proportions (patients: CD, 15.0%; UC, 18.6%; clinicians: CD, 19.5%; UC, 25.0%). Numerically lower SIBDQ scores were observed with suboptimal control (CD, 43.0 ± 10.8; UC, 42.5 ± 12.0) than with optimal control (CD, 58.2 ± 7.2; UC, 57.8 ± 6.6). CONCLUSIONS: Approximately 50% (CD) and 40% (UC) of patients from real-world Canadian practices had suboptimal disease control based on STRIDE-II criteria. Suboptimal control was underestimated by patients and clinicians and accompanied by reduced QoL, suggesting further efforts to implement STRIDE-II treat-to-target strategies are needed.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".