The Stable Microbubble Test for Determining Continuous Positive Airway Pressure (CPAP) Success in Very Preterm Infants Receiving Nasal CPAP from Birth
Bibliographic record
Abstract
<b><i>Background:</i></b> Very preterm infants can be treated with nasal continuous positive airway pressure (CPAP) from birth, but some fail. A rapid test, such as the stable microbubble test (SMT) on gastric aspirate, may identify those who can be managed successfully using CPAP. <b><i>Objective:</i></b> To determine if SMT can identify soon after birth, very preterm infants who may be successfully managed on CPAP alone. <b><i>Methods:</i></b> Stable microbubbles (diameter <15 µm) were counted in gastric aspirates taken <1 h of age from infants <30 weeks' gestation, who received CPAP from birth. Infants failed CPAP if intubated at <72 h of age. Clinicians were masked to SMT results. A receiver operating characteristic curve was generated to determine the relationship between number of microbubbles/mm<sup>2</sup> and subsequent intubation. <b><i>Results:</i></b> 68 infants of mean (SD) 28.1 (1.4) weeks' gestation received CPAP in the delivery room at a median (interquartile range) pressure 7 (6-8) cmH<sub>2</sub>O and FiO<sub>2</sub> 0.25 (0.21-0.3). Gastric aspirates were taken at a median (interquartile range) age of 0.5 (0.3-0.6) hours. The best cut-off point for predicting CPAP success or failure was a SMT count of 8 microbubbles/mm<sup>2</sup>. The area under the receiver operating characteristic curve was 0.8 (95% CI 0.7-0.9). A SMT count ≥8 microbubbles/mm<sup>2</sup> had a sensitivity of 53%, a specificity of 100%, a positive predictive value of 100%, and a negative predictive value of 60% for predicting CPAP success. <b><i>Conclusion:</i></b> Infants treated with CPAP from birth, who had<b> </b>SMT counts ≥8 microbubbles/mm<sup>2</sup> on their gastric aspirate, did not fail CPAP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".