A practical, evidence-based guide to the use of adalimumab in Crohn’s disease
Bibliographic record
Abstract
BACKGROUND: Anti-tumor necrosis factor (anti-TNF) agents are important therapies for treating Crohn's disease (CD) because they may induce and maintain remission, reduce the need for corticosteroids, decrease hospitalizations and surgeries, and heal the mucosa. Here we provide a practical, evidence-based guide to help clinicians optimize the use of adalimumab in patients with CD. SCOPE: A literature search in the MEDLINE, EMBASE, and BIOSIS databases was performed for articles published between 1996 and 2010 describing adalimumab use in CD. Abstracts presented at the ACG, DDW, UEGW, ECCO, and SGNA congresses, references from review articles and published randomized clinical trials, and the manufacturer's prescribing information also were reviewed. FINDINGS: When selecting an anti-TNF agent, factors such as efficacy, safety, immunogenicity, patient preference, and the timing and sequencing of therapies should be considered. Important considerations for patient management include dosage selection, use of combination therapy, timing of monitoring treatment response, and evaluation of recurrent CD symptoms in a previously responding patient. We recommend that patients initiating adalimumab receive a loading dose of 160/80 mg subcutaneously at Week 0/Week 2, followed by up to 8 weeks of 40 mg every-other-week maintenance therapy prior to determining if there is non-response. During therapy, recurrent or new symptoms should be fully evaluated to ensure that they are indeed related to underlying inflammation versus other causes (e.g., intercurrent infection, bile acid diarrhea, or irritable bowel). Patients experiencing attenuation of response or inflammatory-mediated symptoms during maintenance therapy may benefit from dosage intensification to weekly adalimumab. CONCLUSION: Considerations for the use of anti-TNF agents in CD, with an emphasis on adalimumab, are reviewed and practical patient management recommendations are presented.
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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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.020 | 0.025 |
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".