Use of narcotics and sedatives among very preterm infants in neonatal intensive care units in China: an observational cohort study
Bibliographic record
Abstract
Background: Narcotics and sedatives are widely used in neonatal intensive care units for very preterm infants. This study aimed to describe the current use of narcotics and/or sedatives among very preterm infants in Chinese neonatal intensive care units, with an emphasis on infants on invasive mechanical ventilation, and to investigate the association of exposure to narcotics and/or sedatives with neonatal outcomes. Methods: weeks and admitted to 57 tertiary neonatal intensive care units in the Chinese Neonatal Network in 2019. A multivariate logistic regression model was used to assess the association between narcotics and/or sedatives exposure and major neonatal outcomes. Results: Among 9,442 very preterm infants enrolled, 1,566 (16.6%) received at least one dose of narcotics or sedatives, 111 (1.2%) received only narcotics, 1,301 (13.8%) received sedatives solely, and 154 (1.6%) received both narcotics and sedatives during their hospital stay. Of 4,172 very preterm infants who underwent invasive mechanical ventilation, 1,117 (26.8%) received at least one dose of narcotics or sedatives, with 883 (21.2%) only received sedatives. Significant site variation of narcotics/sedatives use existed among hospitals, with the application rate ranging from 0-72.5% in individual hospital. The narcotics and/or sedatives use by very preterm infants was independently associated with increased risks for periventricular leukomalacia, severe retinopathy of prematurity, and bronchopulmonary dysplasia. Conclusions: Narcotic and/or sedative administration is relatively conservative for very preterm infants in Chinese neonatal intensive care units, with significant variation among hospitals. Since narcotic and sedative use might be related to adverse neonatal outcomes, a pressing and developing need for national quality improvement initiatives is seen with respect to pain/stress management for very preterm infants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".