The safety and efficacy of adalimumab in patients with Crohn's disease: the experience of a single Canadian tertiary care centre
Bibliographic record
Abstract
BACKGROUND. Adalimumab (ADA), an antitumor necrosis factor (anti-TNF) monoclonal antibody, is effective in treating moderate-to-severely active Crohn's disease (CD). ADA has been associated with a variety of adverse events (AE). The purpose of this study is to determine the safety and efficacy of ADA in CD patients in clinical practice. METHODS. A retrospective analysis was performed on CD patients treated with ADA. Data extracted and analyzed included patient and CD demographics, remission and response rates with ADA, and safety and tolerability of ADA. RESULTS. A total of 149 ADA-treated CD patients were included. The mean duration of therapy with ADA was 20 months with 32% of patients discontinuing treatment. Anti-TNF-naïve and anti-TNF-exposed patients on ADA achieved clinical remission in 45% and 32%, had a clinical response in 23% and 23%, and had no clinical response in 32% and 45%, respectively. Anti-TNF-naïve and anti-TNF-exposed patients maintained remission in 82% and 67%, respectively. Fistulas healed in 19% and improved in 19%. AE occurred in 38% of patients with infection being the most common (20%). Serious infections lead to death in one (<1%). Logistic regression of AE did not identify statistically significant predictors except for colonic disease location (odds ratio [OR] = 0.31, 95% CI = 0.12-0.82, p = 0.018) and the rate of ADA discontinuation (OR = 3.24, 95% CI = 1.58-6.64, p = 0.0013). CONCLUSION. ADA is an effective treatment for CD. AE can occur commonly leading to discontinuation of medication and may be influenced by disease location. Although serious complications are rare, close monitoring of all patients on ADA is needed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".