Indicators of suboptimal tumor necrosis factor antagonist therapy in inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND: Inflammatory bowel disease (IBD) is refractory to treatment in one-half of patients. AIMS: To evaluate the occurrence of suboptimal therapy among patients with IBD treated with tumor necrosis factor antagonists (anti-TNFs). METHODS: A multinational chart review in Europe and Canada was conducted among IBD patients diagnosed with ulcerative colitis (UC) or Crohn's disease (CD) who initiated anti-TNF therapy between 2009 and 2013. The primary endpoint was the cumulative incidence of suboptimal therapy during a two-year follow-up period, defined by the presence of the following indicators: dose escalation, discontinuation, switching, non-biologic therapy escalation, or surgery. RESULTS: The study included 1195 anti-TNF initiators (538 UC and 657 CD). The majority of patients (64% of UC and 58% of CD) had at least one indicator of suboptimal therapy. The median time to suboptimal therapy indicator was 12.5 and 17.5 months for UC and CD patients, respectively. Among the 111 UC and 174 CD anti-TNF switchers, 51% and 56% had an indicator of suboptimal therapy, respectively. The median time to suboptimal therapy indicator with the second anti-TNF was 14.3 and 13.0 months for UC and CD patients, respectively. CONCLUSION: The majority of IBD patients showed suboptimal therapy with current anti-TNFs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".