Airway injury and pneumomediastinum associated with less invasive surfactant administration in a premature neonate: a case report
Bibliographic record
Abstract
BACKGROUND: The use of less invasive surfactant administration (LISA)/minimally invasive surfactant therapy (MIST) has increased due to its potential advantage over traditional surfactant delivery methods through an endotracheal tube. Known complications for this procedure include failure of the first attempt at insertion, desaturation, and bradycardia. To the best of our knowledge, this is the first reported case of pneumomediastinum and subcutaneous emphysema following LISA. CASE PRESENTATION: A preterm newborn born at 27 weeks of gestation presented with respiratory distress syndrome requiring surfactant replacement. LISA using the Hobart method was completed. There was a report of procedural difficulty related to increased resistance to insertion of the 16G angiocath. The newborn was subsequently noted to have subcutaneous emphysema over the anterior aspect of the neck and substantial pneumomediastinum on radiological assessment. Associated complications included hypotension requiring inotropic support. The newborn was successfully managed conservatively, with complete resolution of the air leak. CONCLUSIONS: Upper airway injury leading to air leak syndrome is a rare complication of the Hobart method for LISA. Awareness of such procedural complications is important as the use of the LISA method increases.
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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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".