High-volume surfactant administration using a minimally invasive technique: Experience from a Canadian Neonatal Intensive Care Unit
Bibliographic record
Abstract
BACKGROUND: Surfactant delivery via a thin endotracheal catheter during spontaneous breathing, a technique called minimally invasive surfactant therapy (MIST), is an alternative to intubation and surfactant administration. There is paucity of data regarding the administration of high-volume surfactant using this technique. METHODS: We conducted a retrospective cohort study to review the safety, efficacy, and procedural details pertaining to the delivery of 5 mL/kg of BLES® via MIST approach. In 2016, our centre initiated a practice change allowing the use of MIST as an alternative method of surfactant delivery in infants born at ≥28 weeks and/or with a birth weight ≥ 1,000 g with respiratory distress syndrome. In this study, we identified all neonates who received surfactant via MIST between May 1, 2016 and July 30, 2018 and collected relevant procedural data. RESULTS: Since this practice change, MIST technique was attempted in 43 neonates with successful instillation of surfactant in 41 (95.3%) of the neonates. Intubation and positive pressure ventilation was avoided in 35 neonates (85.3%). No serious adverse effect was noted. CONCLUSIONS: Our study reports successful use of higher volume surfactant via MIST. This should encourage other similar centres to consider this technique, in order to avoid unnecessary intubation and positive pressure ventilation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".