Long‐term outcome of a third anti‐<scp>TNF</scp> monoclonal antibody after the failure of two prior anti‐<scp>TNF</scp>s in inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND: A significant proportion of patients with IBD lose response to anti-TNF therapies. There is limited knowledge of the long-term outcomes of those who have failed two anti-TNF agents and commenced a third. AIM: To examine the safety and efficacy of third anti-TNF treatment after failure of two prior anti-TNF agents in patients with inflammatory bowel disease. METHODS: This was a retrospective study of all IBD patients [Crohn's disease (CD), ulcerative colitis (UC)] treated with a third anti-TNF agent after loss of response or intolerance to two prior anti-TNF agents at a single tertiary North American centre. Disease activity, drug therapy and Montreal phenotypes were noted at disease onset and commencement of the third anti-TNF agent. Kaplan-Meier estimates were used to calculate the probability of remaining on the third anti-TNF agent and to identify predictors of long-term clinical response. RESULTS: A total of 63 patients (64% women, 57 CD and 6 UC) were included in the analysis. The mean disease duration at initiation of third anti-TNF was 12 years. Thirty-five (55.6%) patients discontinued the third anti-TNF after a mean of 13.2 months. Probability of remaining on the third anti-TNF was 0.69, 0.55, 0.37 and 0.25 at 6, 12, 24 and 36 months respectively. Prior primary nonresponders to the first anti-TNF agent [hazard ratio (HR) 6.4, 95% CI 2.5-16.1] and persistent disease activity at 3 months after commencement of a third anti-TNF (HR 3.2, 95% CI 1.3-7.8) predicted poorer response. CONCLUSIONS: Over half of patients with inflammatory bowel disease, initiated on a third anti-TNF agent after failure of two prior anti-TNF drugs, are able to remain on the third anti-TNF at 1 year.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".