The Stable Microbubble Test for Determining Continuous Positive Airway Pressure (CPAP) Success in Very Preterm Infants Receiving Nasal CPAP from Birth
Bibliographic record
Abstract
BACKGROUND: 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. OBJECTIVE: To determine if SMT can identify soon after birth, very preterm infants who may be successfully managed on CPAP alone. METHODS: 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(2) and subsequent intubation. RESULTS: 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) cmH2O and FiO2 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(2). The area under the receiver operating characteristic curve was 0.8 (95% CI 0.7-0.9). A SMT count ≥8 microbubbles/mm(2) 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. CONCLUSION: Infants treated with CPAP from birth, who had SMT counts ≥8 microbubbles/mm(2) on their gastric aspirate, did not fail CPAP.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".