A157 REAL-WORLD TIGHT OBJECTIVE MONITORING WITH ADALIMUMAB LEADS TO EARLIER DOSE OPTIMIZATION AND HIGHER CLINICAL REMISSION RATES AT 12 MONTHS.
Bibliographic record
Abstract
Abstract Background Data suggests that tight objective monitoring of inflammatory bowel diseases (IBD) may improve one-year clinical outcomes. Aims The goal of this study is to assess the adherence to serial tight objective monitoring, via clinical symptoms and biomarkers, and the effect of such tight monitoring on one year outcome in IBD patients at an academic and an university-affiliated center. Methods We retrospectively reviewed the chart of 428 consecutive IBD patients who started adalimumaby at the McGill University Health Center and Jewish General Hospital (Montreal, Canada) between January 1, 2015 and January 1, 2019 [338 Crohn’s disease(CD), 90 ulcerative colitis(UC)]. Clinical symptoms (assessed by Harvey-Bradshaw-Index and partial Mayo), C-Reactive Protein(CRP), and fecal calprotectin(FCAL) were captured at treatment initiation and at 3, 6, 9, and 12 months. Combined adherence was defined as the evaluation of ≥2 of 3 parameters(clinical, CRP, FCAL). Dose optimization and drug sustainability curves were plotted by Kaplan-Meier method. Results Clinical symptoms were assessed in nearly all patients at 3 (CD-UC:95-94%), 6 (90-83%), 9 (86-85%) and 12 (96-89%) months. CRP was also available for most patients but the frequency of assessment decreased in CD patients over the study period. In comparison, compliance to serial FCAL testing was low throughout the follow-up period. Clinical remission at one-year was significantly higher in patients who were adherent to early assessment visit at 3 months (p=0.001 both for CD and UC). Adherence to early follow-up also resulted in earlier dose optimisation in both CD and UC patients(pLogrank=0.026 for UC and p=0.09 for CD). However, the overall drug sustainability did not differ. Conclusions Clinical assessment and CRP, but not FCAL, were frequently assessed in patients starting adalimumab. Adherence to early objective combined follow-up visits resulted in earlier dose optimization and improved one-year clinical outcomes but did not change drug sustainability rates. Funding Agencies None
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".