P552 Assessing adherence to objective disease monitoring and outcomes with adalimumab in a real-world IBD cohort
Bibliographic record
Abstract
Abstract Background Data suggests that tight objective monitoring may improve clinical outcomes in IBD. Methods The aim of this study is to assess the adherence to serial tight objective monitoring(clinical and biomarkers) and its effect on clinical outcomes. We retrospectively reviewed the chart of 428 consecutive IBD patients started on adalimumab between January 1,2015–January 1,2019 [338 Crohn’s disease(CD), 90 ulcerative colitis(UC)]. Clinical symptoms (assessed by Harvey-Bradshaw-Index, partial Mayo score), C-Reactive Protein(CRP), and fecal calprotectin(FCAL) assessments were captured at treatment initiation and at 3, 6, 9, and 12 months. Dose optimization and drug sustainability curves were plotted by Kaplan-Meier method Results Clinical evaluation was available 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 in nearly all patients but testing frequency decreased in CD patients over time. Compliance to serial FCAL testing was low. Clinical remission at one-year was higher in patients adherent to early assessment visit at 3 months (p=0.001 for CD and UC). Adherence to early follow-up resulted in earlier dose optimisation in CD and UC patients (pLogrank=0.026 for UC & p=0.09 for CD). Overall drug sustainability did not differ. Conclusion Clinical 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, improved one-year clinical outcomes but did not change drug sustainability.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".