ADALIMUMAB FOR ULCERATIVE COLITIS: RESULTS OF A BRAZILIAN MULTICENTER OBSERVATIONAL STUDY
Bibliographic record
Abstract
BACKGROUND: Adalimumab is a monoclonal antibody, tumor necrosis factor-alpha (TNFα) inhibitor that has efficacy for inducing and maintaining remission in moderate-to-severe ulcerative colitis. Real world studies with adalimumab in Latin American ulcerative colitis patients are scarce. OBJECTIVE: To assess the clinical remission rates in induction and maintenance with adalimumab therapy in ulcerative colitis. METHODS: Observational, multicenter and retrospective study on a case series of patients with moderate-to-severe ulcerative colitis under adalimumab therapy. The variables analyzed were: demographic data, previous infliximab status, concomitant drugs, the Montreal Classification, disease activity (Mayo score) at weeks 0, 8, 26 and 52, or until the last follow-up. Clinical remission was defined as a partial Mayo score ≤2 and Last observation carried forward (LOCF) and Non responder imputation (NRI) analysis were used. RESULTS: Thirty-six patients were included in the study. With LOCF analysis, remission rates at weeks 8, 26 e 52 were of 41.7%, 47.2% and 47.2%, respectively. With NRI analysis, remission rates at weeks 8, 26 and 52 were of 41.7%, 41.7% and 27.8%, respectively. CONCLUSION: Adalimumab was effective in the treatment of moderate-to-severe ulcerative colitis. Clinical remission was observed in approximately 40% of the patients at weeks 8 and 26, and in almost a quarter of the patients after 1 year of follow up.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".