The utility of H2 receptor antagonists in preventing infusion‐related reactions to paclitaxel chemotherapy
Bibliographic record
Abstract
BACKGROUND: Paclitaxel has a risk of infusion-related reactions (IRRs) and despite no prospective evidence, is often given with premedication including a corticosteroid, H1 antagonist, and H2 antagonist (H2RA). Backorders impacted the supply of intravenous H2RAs at our center, and it was removed as routine premedication. The authors compared the incidence of IRR in patients treated without H2RA to patients receiving standard H2RA premedication. METHODS: The authors reviewed outpatients starting paclitaxel at the Ottawa Hospital from December 2019 to October 2021. Two cohorts were created: patients treated without H2RA premedication (intervention), and those receiving standard H2RA (control). Demographics, treatment, and IRR information were collected retrospectively. Primary end point was rate of grade ≥2 IRRs during first two doses of paclitaxel. RESULTS: A total of 182 patients were treated without H2RA premedication, compared to 184 control patients treated during non-backorder periods. Baseline characteristics included: median age, 63 years; 86% female; and primary tumor 52% breast/24% gynecologic/10% gastric/esophageal/8% lung/6% other. There were no significant differences between cohorts in baseline characteristics. There was no difference in the rate of grade ≥2 IRR between cohorts; 12.1% (22 of 182; 95% confidence interval [CI], 7.7%-17.7%) for patients treated without H2RA, and 15.1% (28 of 185; 95% CI, 10.3%-21.1%) for control patients. The rate of grade ≥3 IRRs were also similar, 4.4% in intervention cohort versus 3.8% in control cohort. CONCLUSIONS: The removal of H2RAs from premedication for paclitaxel did not result in an increased incidence of IRRs. The use of H2RAs in preventing IRRs to paclitaxel should be re-evaluated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".