Narcotics and sedatives use in the mechanical ventilation in preterm infants: Predictors and outcome
Bibliographic record
Abstract
BACKGROUND: Mechanical ventilation (MV) causes discomfort but whether it causes pain remains controversial. Around the world neonatal intensive care units (NICU) often utilize narcotics and/or sedatives during MV of vulnerable infants yet the association with adverse neonatal outcomes has not been adequately addressed. OBJECTIVE: Test for associations between the use of narcotics/sedatives during MV and mortality/morbidity in preterm infants in a large infant cohort in Canada. DESIGN/METHODS: Preterm infants born <35 weeks gestational age (GA) requiring MV for >24 hrs were identified retrospectively from the Canadian Neonatal Network database, 2010-12. Infants were categorized according to whether they received narcotics/sedatives for greater than 24 hours concurrently with MV. Infants were excluded if moribund on admission, had major congenital anomalies, diagnoses where narcotic administration is routine and suspected seizures. Multivariable logistic and linear regression analysis tested for association of narcotics/sedatives use during MV with mortality/morbidity (nosocomial infections, BPD, ROP, IVH) and length of MV. RESULTS: After exclusions the cohort included 2672 infants; 467(17%) exposed only to narcotics 101(4%) only to sedatives and 299(11%) to both. All models were adjusted for GA, gender, small for GA, SNAP-II score >20, multiple births, delivery mode, outborn, PDA status, MV type, use of high flow, muscle relaxant use, indwelling lines, caffeine and surfactant therapy. The composite mortality/morbidity, and MV days were significantly higher for MV infants exposed to narcotics, sedatives or both compared to infants not exposed. CONCLUSION: Mounting evidence of the adverse short and long-term impacts of narcotics/sedatives during MV supports the need for further work in alternative therapies.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".