Efficacy of minimally invasive surfactant therapy in moderate and late preterm infants: A multicentre randomized control trial
Bibliographic record
Abstract
BACKGROUND: Minimally invasive surfactant therapy (MIST) is a new strategy to avoid mechanical ventilation (MV) in respiratory distress syndrome. The primary aim of this study was to test MIST as a means of avoiding MV exposure and pneumothorax occurrence in moderate and late preterm infants (32 to 36 weeks' gestational age). METHODS: in the first 24 hours of life. Patients from the intervention group received MIST immediately after inclusion. The primary outcome was either need for MV or development of a pneumothorax requiring a chest tube. To ensure that clinicians were not biased toward delaying intubation in the intervention group, clinical failure criteria were also used as a primary outcome. The primary outcome was analyzed using bivariate and multivariate logistic regressions. RESULTS: Among 45 randomized patients, 24 were assigned to MIST and 21 to standard management. Eight infants (33%) from the intervention group met the primary outcome criteria versus 19 (90%) in the control group (absolute risk reduction 0.57, 95% confidence interval 0.54 to 0.60). One patient in each group reached the primary outcome because of pneumothorax occurrence. The other patients were exposed to MV. None of the patients reached the clinical failure criteria. CONCLUSION: MIST for respiratory distress syndrome management in moderate and late preterm infants was associated with a significant reduction of MV exposure and pneumothorax occurrence.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".