INTRAVENOUS ACETAMINOPHEN FOR POSTOPERATIVE PAIN IN NEONATES
Bibliographic record
Abstract
Background: Managing pain is challenging, especially in neonates. Uncontrolled pain and opioid exposure are associated with short- and long-term adverse events. Adequately controlling pain while reducing opioid exposure is paramount in the neonatal population. This thesis presents three studies, all aiming to determine if IV acetaminophen is an appropriate adjunct to current opioid-based postoperative pain regimens. The population of interest is neonates admitted to the neonatal intensive care unit (NICU) treated with major abdominal and thoracic surgery. Chapter 1 provides the scientific framework underpinning this work and the rationale for performing the included studies. Chapter 2 presents the results of a systematic review and meta-analysis assessing the effect of IV acetaminophen on postoperative pain in pediatric patients. This chapter further expands on gaps and opportunities for future research. Chapter 3 reports the results of a national survey in which pediatric surgeons, anesthesiologists, and neonatologists reported their postoperative pain prescribing practices in the NICU and their perspectives on the use of IV acetaminophen. Chapter 4 describes the protocol for a pilot randomized controlled trial (RCT). This study will assess the feasibility of a multicenter RCT to evaluate the effectiveness of IV acetaminophen for postoperative pain in neonates recovering from major abdominal and thoracic surgery. Chapter 5 summarizes the results of the studies in context and details how the results of each study informed the others. It also discusses areas of future research.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".