Adherence to Objective Therapeutic Monitoring and Outcomes in Patients with Inflammatory Bowel Disease with Adalimumab Treatment. A Real-world Prospective Study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Objective monitoring and effective early treatment using a treat-to-target approach are key to improving therapeutic outcomes in IBD patients. This study aimed to assess adherence to objective monitoring (clinical, biomarkers, and endoscopy) and its impact on clinical outcomes. METHODS: A prospective, multicenter study included consecutive IBD patients starting on adalimumab therapy between January 2019 and December 2020. Disease activity, assessed by the Harvey-Bradshaw index (HBI), partial Mayo, C-reactive protein (CRP), fecal calprotectin (FCAL), and endoscopy were evaluated at adalimumab initiation and 3, 6, 9 and 12 months. Therapeutic drug monitoring, changes in treatment, drug sustainability, and clinical outcomes were assessed. RESULTS: 104 IBD patients were enrolled (78.8% CD, median age 34.3 years, disease duration 9 years). During the 12 months follow-up, high adherence to clinical activity assessment was observed in both CD (81.3%- 87.7%) and UC patients (76.5-90.9%). CRP measurement decreased over time in both CD (37.3%-54.9%) and UC (29.4%-50.0%). The adherence to serial FCAL monitoring was low in CD (22.7-31.3%) and UC patients (17.6-56.0%). UC patients had higher adherence to early endoscopic assessment (<6 months) compared to CD patients (40.9% vs. 21.5%). Adherence to early combined clinical and biomarkers resulted in earlier dose optimization in CD and UC (log-rank<0.001), but drug sustainability was not different. The patients with early combined adherence had a significantly higher clinical remission rate at 1 year compared to non-adherence (70.2% vs. 29.8%, p=0.007) but no significant difference in UC patients. CONCLUSIONS: The adherence to early objective monitoring with combined clinical and biomarkers assessment in IBD patients starting adalimumab therapy led to dose optimization and improved 1-year clinical remission in CD but did not change drug sustainability and clinical remission in UC.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".