Survey on the use of adalimumab as maintenance therapy in Crohn's disease in England and Ireland
Bibliographic record
Abstract
INTRODUCTION: Adalimumab is effective in inducing and maintaining response/remission in patients with Crohn's disease either naive to biological therapies or after secondary failure of infliximab. AIM: To present the first 'real-life' survey data from England and Ireland on the use of adalimumab. METHOD: A retrospective audit conducted through a web-based questionnaire in England/Ireland. RESULTS: We analysed data on 61 patients (35 female, 26 male) with a median age of 33 years (range 17-71 years) and an average follow-up of 8 months. The maximal maintenance dose was 40 mg every other week in 84% of patients, 40 mg weekly in 13% and 80 mg weekly in 3%. Maintenance adalimumab achieved remission in 57% of patients. The ongoing response rate was 83.6%. An additional 8% had a secondary loss of response after an average of 8.4 months (range 2-17). Adverse effects were observed in 23% of patients: of which there was local pain in 29%, infection in 36%, headaches in 14%, leucopenia (on azathioprine) in 7%, a painful rash in 7% and serum-sickness-type reaction in 7%. Adverse events led to discontinuation in two patients. CONCLUSION: This English/Irish audit shows an acceptable response/remission and safety profile of adalimumab in the treatment of Crohn's disease. In contrast to earlier data from Scotland, dose escalation was only observed in 16% of patients. The majority of responders were steroid-free at 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".