Premedication Use Before Infliximab Administration
Bibliographic record
Abstract
BACKGROUND: Premedications are commonly given to patients with inflammatory bowel disease before intravenous infliximab administration. We aimed to (1) describe practice variability; and (2) determine clinician rationale for premedicating patients with inflammatory bowel disease before infliximab administration. METHODS: We developed a cross-sectional electronic survey after comprehensive literature review to assess practice variability and clinician rationale for premedication use before infliximab. An optional postsurvey quiz assessed clinicians' understanding of the available literature. The survey was distributed through members-only NASPGHAN and Crohn's and Colitis Foundation of America (CCFA) listservs and American Gastroenterological Association (AGA) and American College of Gastroenterology (ACG) web-based discussion boards. RESULTS: Three hundred seventy-nine unique respondents with a 93.3% survey completion rate comprised 331 (87%) and 45 (12%) pediatric and adult gastroenterologists. Among numerous options for premedications, acetaminophen (66%) and diphenhydramine (64%) were most often given before each infliximab infusion. Only 20% did not routinely use premedications. There was heterogeneity of premedication use between gastroenterologists within the same clinical practice. Of 328 (87%) respondents who completed the knowledge assessment quiz, only 18% identified the association of diphenhydramine use with increased reaction. CONCLUSIONS: There is high interpractice and intrapractice variability for premedication use before infliximab administration. Clinician rationale for premedicating patients seems to be driven by individual preference or group practice habit. Improved knowledge of the evidence may assist in decreasing overuse of premedications, particularly diphenhydramine.
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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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".