Gastrointestinal Adverse Drug Reaction Profile of Etanercept: Real-world Data From Patients and Healthcare Professionals
Bibliographic record
Abstract
Objective We aimed to describe the nature and frequency of gastrointestinal adverse drug reactions (GI-ADRs) of etanercept (ETN) using patient-reported and healthcare professional (HCP)-registered data and compared this frequency with the GI-ADR frequency of the widely used tumor necrosis factor-α inhibitor adalimumab (ADA). Methods Reported GI-ADRs of ETN for rheumatic diseases were collected from the Dutch Biologic Monitor and DREAM registries. We described the clinical course of GI-ADRs and compared the frequency with ADA in both data sources using Fisher exact test. Results Out of 416 patients using ETN for inflammatory rheumatic diseases in the Dutch Biologic Monitor, 25 (6%) patients reported 36 GI-ADRs. In the DREAM registries 11 GI-ADRs were registered for 9 patients (2.3%), out of 399 patients using ETN, with an incidence of 7.1 per 1000 patient-years. Most GI-ADRs consisted of diarrhea, nausea, and abdominal pain. GI-ADRs led to ETN discontinuation in 1 patient (4%) and dose adjustment in 4 (16%) in the Dutch Biologic Monitor. Eight GI-ADRs (73%) led to ETN discontinuation in the DREAM registries. The frequency of GI-ADRs of ETN did not significantly differ from GI-ADRs of ADA in both data sources (Dutch Biologic Monitor: ETN 8.7% vs ADA 5.3%,P= 0.07; DREAM: ETN 2.8% vs ADA 4.7%,P= 0.16). Conclusion Most GI-ADRs associated with ETN concerned gastrointestinal symptoms. These ADRs may lead to dose adjustment or ETN discontinuation. The frequency of ETN-associated GI-ADRs was comparable to the frequency of ADA-associated GI-ADRs. Knowledge about these previously unknown ADRs can facilitate early recognition and improve patient communication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".