Treatment of ulcerative colitis with adalimumab or infliximab: long‐term follow‐up of a single‐centre cohort
Bibliographic record
Abstract
Aliment Pharmacol Ther 2010; 32: 522-528 Summary Background Randomized, controlled trials have demonstrated that anti-TNF agents are efficacious in inducing remission in cases of Crohn's disease and ulcerative colitis. However, response rates for anti-TNF agents in 'real life' clinical practice are less well-defined. Aims To examine the response rates and long-term outcomes of infliximab and adalimumab treatment for out-patients with ulcerative colitis and to study the variables associated with response rates. Methods In a prospective study, a single-centre out-patient cohort was treated and followed up according to a structured protocol of clinical care. Response to treatment was assessed using a physician's global assessment that focused on normalization of bowel frequency, absence of blood with defecation and tapering of corticosteroids to zero. Results Fifty-three ulcerative colitis patients were included in the study. Responses to induction therapy were 96.4% (27/28) for infliximab and 80% (20/25) for adalimumab (P = 0.0889). Responses to maintenance therapy were similar: infliximab 77.8% (14/18) and adalimumab 70.0% (14/20) (P = 0.7190). Multivariate analyses of the induction and maintenance responses did not reveal confounding elements. No new safety signals were identified. Conclusions This long-term follow-up of a single-centre cohort of ulcerative colitis patients demonstrates that 'real-life' out-patient treatment with infliximab and adalimumab is effective in induction and maintenance of response.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".