Premedication for neonatal intubation: Current practice in Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: Despite strong evidence of the benefits of rapid sequence intubation in neonates, it is still infrequently utilized in neonatal intensive care units (NICU), contributing to avoidable pain and secondary procedure-related physiological disturbances. OBJECTIVES: The primary objective of this cross-sectional survey was to assess the practice of premedication and regimens commonly used before elective endotracheal intubation in NICUs in Saudi Arabia. The secondary aim was to explore neonatal physicians' attitudes regarding this intervention in institutions across Saudi Arabia. METHODS: A web-based, structured questionnaire was distributed by the Department of Pediatrics, Umm Al Qura University, Mecca, to neonatal physicians and consultants of 10 NICUs across the country by E-mail. Responses were tabulated and descriptive statistics were conducted on the variables extracted. RESULTS: 85% responded to the survey. Although 70% believed it was essential to routinely use premedication for all elective intubations, only 41% implemented this strategy. 60% cited fear of potential side effects for avoiding premedication and 40% indicated that the procedure could be executed more rapidly without drug therapy. Treatment regimens varied widely among respondents. CONCLUSION: Rates of premedication use prior to non-emergent neonatal intubation are suboptimal. Flawed information and lack of unified unit policies hampered effective implementation. Evidence-based guidelines may influence country-wide adoption of this practice.
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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 | 0.005 |
| 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 | 0.000 |
| 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 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".