Predicting successful nasal continuous positive airway pressure treatment in newborn infants: a multivariate analysis
Bibliographic record
Abstract
The use of nasal continuous positive airway pressure (nCPAP) in newborn infants is common, especially for weaning after mechanical ventilation. We have reported on the successful transition to the use of the infant flow method as a standard of practice in Poland. The authors present results of multivariate logistic regression (MLR) analysis of 481 newborns treated with the infant flow method in an effort to improve related clinical guidance. We collected data on the baseline demographic, physiological characteristics and outcomes of 1,299 newborns treated with nCPAP in 57 neonatal ICUs in Poland over a 2-year period. We conducted a stepwise MLR of 481 newborns with the two most common indications for use. We evaluated three outcomes: need for intubation in newborns treated electively with nCPAP (RDS), weaning failure requiring reintubation in the mechanically ventilated newborns (weaning), and bad outcome. In the RDS group of patients we found that nCPAP failure was highly significantly related to estimated gestational age and clinical risk index for babies (CRIB). While in our population less mature RDS newborns were only slightly less likely to avoid intubation, the MLR model showed that, controlling for initial CRIB, they were less than one-half as likely to avoid intubation. Failure of nCPAP in weaning was highly significantly related to only pH, prior to beginning nCPAP. Bad outcomes were highly related to estimated gestational age and CRIB in the RDS group, but not the weaning population. We believe that understanding the risk of both nCPAP failure and also bad outcomes for a specific patient will enhance clinical decision-making. That is, for patients with the highest risk of poor outcome or nCPAP failure, more aggressive use of intubation and surfactant might be warranted. Likewise, such aggressive therapy might also be avoided for those with a seemingly low chance of poor outcome.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".