Objective Treatment Targets and Their Correlation with Patient-Reported Outcomes in Inflammatory Bowel Disease: A Real-World Study
Bibliographic record
Abstract
Background & Aims: treat-to-target approach is essential for improving outcomes in inflammatory bowel disease (IBD). This study aimed to assess real-world achievement in objective monitoring (clinical, biomarker, and endoscopic assessments) and the correlation between patient-reported outcomes (PROs) and treatment targets. Methods: This retrospective study included consecutive IBD patients from January 2020 to December 2024. Disease activity was assessed using the Harvey-Bradshaw Index (HBI), partial Mayo score, PRO2, and PRO3, along with C-reactive protein (CRP) levels and endoscopic scores (SES-CD, MES). Clinical outcomes were evaluated at baseline, 1 year, and 2 years. Results: Among 112 IBD patients (55% with CD, median age at diagnosis: 45.2 years), clinical remission rates at baseline, 1 year, and 2 years were; CD: 75.8%, 70.0%, and 55.8%; UC: 84.0%, 79.5%, and 81.4%. CRP normalization rates at the same time points were; CD: 54.8%, 41.7%, and 63.8% UC: 78.0%, 70.5%, and 81.8%. Endoscopic remission rates were; CD: 58.1%, 50.0%, and 50.0%, UC: 71.4%, 64.5%, and 51.7% Flare-ups were more frequent in CD than in UC (32% vs. 20%), with an 8.1% rate of IBD-related surgery. In CD, PRO2 and PRO3 strongly correlated with clinical remission (AUC = 0.885 and 0.881), moderately with biomarkers (AUC = 0.737 and 0.755), and modestly with endoscopic remission (AUC = 0.695 and 0.685). In UC, PRO2 showed a strong correlation with clinical remission (AUC = 0.972) and moderate correlations with biomarkers (AUC = 0.653) and endoscopy (AUC = 0.783). Conclusions: Clinical remission was more frequent in UC than in CD. PROs showed a strong correlation with clinical remission but only moderate associations with biomarkers and endoscopic remission in both CD and UC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".