Infliximab-graded challenge in a patient with Crohn’s disease and adalimumab hypersensitivity
Bibliographic record
Abstract
Infliximab and adalimumab are monoclonal antibodies to tumor necrosis factor alpha (TNF-α) used in the treatment of various inflammatory disorders. Infliximab, a chimeric monoclonal antibody, is postulated to be more immunogenic as it is not entirely humanized. Despite reports of adalimumab treatment in patients after an adverse reaction to infliximab, there is a paucity of literature reporting the converse – treatment with infliximab after adverse reaction to adalimumab. Thus, it is difficult to estimate the risk of cross-sensitization. Graded drug challenges are utilized for patients unlikely to be allergic to a specific drug, but where concern for a reaction remains. To present a patient with Crohn’s disease and prior hypersensitivity to adalimumab who successfully underwent a graded intravenous challenge with infliximab. The patient previously had acute generalized urticaria due to adalimumab, with corresponding positive intradermal skin tests. Because her bowel disease activity was severe, her gastroenterologist preferred to start another anti-TNF agent. Infliximab was the chosen alternative. She underwent an infliximab-graded challenge in an outpatient clinic staffed by trained allergists. The patient received infliximab during the graded challenge without adverse reactions. This is the first case, to our knowledge, to demonstrate an infliximab-graded challenge for a patient with a prior reaction to adalimumab. For patients requiring TNF-α inhibitors, but with previous reactions and concern for cross-sensitization, a graded challenge with the first dose of an alternate agent under observation by care providers trained to manage adverse drug reactions, may be a safe approach.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".