Intravenous Acetaminophen: An Alternative to Opioids for Pain Management?
Bibliographic record
Abstract
> You are consulted to co-manage a 7-month-old girl after surgical reduction of intussusception. The surgery was uncomplicated, and the patient was extubated in the operating room. Pain management will consist of intravenous (IV) opioids, specifically morphine. IV acetaminophen has recently become available at your institution, and you wonder if it could reduce the need for morphine in this patient. Would this then minimize the potential for adverse effects? IV acetaminophen, under the brand name Ofirmev®, was approved in 2010 by the US Food and Drug Administration for the management of mild to moderate pain, the management of moderate to severe pain with adjunctive opioid analgesics, and the reduction of fever in patients aged ≥2 years at a dosage of 15 mg/kg every 6 hours to a maximum of 75 mg/kg per day.1 The cost for a single-use vial is about $10. Although there are 10 years of worldwide experience, the use of IV acetaminophen in North America has remained limited despite growing evidence supporting its benefits and relative safety. Use in infants has been particularly limited due to its off-label status. In the double-blind, randomized controlled trial recently published in JAMA ,2 Ceelie et al investigated the utility of IV acetaminophen in infants. More specifically, they sought to determine whether the routine use of IV acetaminophen would reduce postoperative morphine requirements in patients …
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".