Second-line strategies after anti-TNF failure in chronically active, moderate-to-severe ulcerative colitis: a retrospective, multicentre cohort study
Bibliographic record
Abstract
Background Many ulcerative colitis (UC) patients require the use of second-line agents after the failure of anti-TNF therapy.Research design and methods We conducted a multicenter, retrospective study including 683 chronically active, moderate-to-severe UC patients who failed first-line anti-TNFs. The rate of treatment persistence and colectomy-free survival was assessed up to 3 years after the initiation of second-line therapy. Predictors for colectomy and persistence were investigated.Results After the failure of first-line anti-TNF, ustekinumab had superior persistence and colectomy-free survival rates compared to tofacitinib (p = 0.05; p = 0.001) and vedolizumab (p = 0.02; p = 0.05), but significant difference was only found in persistence rates in comparison with anti-TNFs (p < 0.001). Regardless of the number of prior anti-TNFs, significantly higher persistence (p = 0.05) and colectomy-free survival rates (p = 0.01) were observed over 2 years with ustekinumab than with vedolizumab or tofacitinib, whereas ustekinumab’s superiority over tofacitinib seemed to disappear by the third year. Hypoalbuminaemia (p = 0.002) and shorter disease duration at second-line initiation (p = 0.03) increased, while concomitant immunomodulators (p = 0.05) reduced the risk for colectomy. Shorter disease duration (p = 0.01) and primary non-response to the previously used anti-TNF (p < 0.001) negatively influenced persistence with second-line non-TNF-targeted agents.Conclusion After first-line anti-TNF failure, switching to a non-anti-TNF agent is worth considering in moderate-to-severe 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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".