Use of naloxone to minimize extubation failure after premedication for INSURE procedure in preterm neonates
Bibliographic record
Abstract
OBJECTIVES: A new guideline for the early respiratory management of preterm infants that included early nCPAP and INSURE was recently introduced in our NICU. This case series describes the clinical courses of a group of preterm infants managed according to this guideline, and reports the rates of successful extubation within 30 minutes of surfactant administration with and without the use of naloxone and adverse events encountered. STUDY DESIGN: Descriptive case series of all preterm babies admitted to our unit who were candidates for INSURE procedure with premedication from August 2012 to August 2013. RESULTS: A total of 31 infants were included with a mean birth weight of 1178 grams and a mean gestational age of 28.4 weeks. Twelve out of thirteen (92%) infants in the naloxone group were extubated within 30 minutes of surfactant administration while only 12/18 (67%) in the non-naloxone group were extubated within the same time frame. No adverse reactions were noted with naloxone usage in this context. CONCLUSION: Naloxone can be effective in reversing the respiratory depressive effect of analgesic premedication and in turn facilitates expeditious extubation in some preterm infants intubated for INSURE procedure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 | 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".