Premedications for infliximab infusions do not impact the risk of acute adverse drug reactions
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to identify the association of premedication with the adverse drug reaction (ADR) rate in infliximab infusions. DESIGN: A retrospective chart review of 684 patients who received 4077 infusions in a network of community clinics over 16.5 months. Data collected included age, weight, sex, diagnosis, dose, premedications and ADR information, which was coded for time of onset, severity and outcome. SETTING: Community infusion clinics located in Ontario, Canada. PATIENTS: Patients aged 12-91 years who receive regular infliximab infusions to treat their autoimmune condition, mainly either Crohn's disease or rheumatoid arthritis. MAIN OUTCOME MEASURES: The number of infusions to the occurrence of an acute ADR by presence of premedication. RESULTS: (1, n=644, p=0.651)=0.204) between those who always received premedications and those who never did. When controlling for age, sex, weight and diagnosis, patients receiving premedications were just as likely to experience an ADR as patients who never received any (OR 1.1, 95% CI 0.7 to 1.9, p=0.5). When assessing the number of infusions to the occurrence of an ADR using the Kaplan-Meier method, no significant difference was found between the two groups. CONCLUSIONS: The use of premedications is not associated with a decreased risk of ADR in patients receiving infliximab. This held true for patients who had never had an ADR prior to receiving premedications and while controlling for age, sex, weight and diagnosis.
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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".