Comparative analysis of adverse events between infliximab and adalimumab in Crohn's disease management: a Brazilian single-centre experience
Bibliographic record
Abstract
Introduction Data is scarce regarding adverse events (AE) of biological therapy used in the management of Crohn's Disease (CD) among Brazilian patients. Objectives To analyse AE prevalence and profile in patients with CD treated with Infliximab (IFX) or Adalimumab (ADA) and to verify whether there are differences between the two drugs. Method Retrospective observational single-centre study of CD patients on biological therapy. Variables analysed: Demographic data, Montreal classification, biological agent adminis- tered, treatment duration, presence and type of AE and the need for treatment interruption. Results Forty-nine patients were analysed, 25 treated with ADA and 24 with IFX. The groups were homogeneous in relation to the variables studied. The average follow-up period for the group treated with ADA was 19.3 months and 21.8 months for the IFX group (p = 0.585). Overall, 40% (n = 10) of patients taking ADA had AE compared with 50% (n = 12) of IFX users (p = 0.571). There was a tendency towards higher incidence of cutaneous and infusion reac- tions in the IFX group and higher incidence of infections in the ADA treated group, although without significant difference. Conclusions No difference was found in the AE prevalence and profile between ADA and IFX CD patients in the population studied.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".