A Simplified and Data‐Driven Lung Ultrasound Approach for Predicting Surfactant Need in Preterm Infants
Bibliographic record
Abstract
OBJECTIVES: Six-region lung ultrasound (LUS) scores show good predictive value for predicting surfactant need in preterm infants but rely on a fixed threshold, which may lead to misclassification near the cut-off and lack data-driven justification for selecting these 6 regions. This study explored whether evaluating individual regions-and combinations-could improve predictive accuracy and utility. METHODS: Data from preterm infants born at ≤34 weeks and enrolled in the Serial Lung Ultrasound for Surfactant Replacement Therapy (SLURP) cohort study were analyzed to develop predictive models for surfactant administration based on regional LUS scores. Univariate, bivariate, and machine learning analyses were conducted to identify the most informative lung regions. Rule-based, decision tree, and logistic regression models were then developed, compared to the 6-region model, and validated on an external dataset. RESULTS: The training set consisted of 77 patients from the SLURP cohort study. The rule-based, decision tree, and logistic regression models showed the best performance, primarily using 2 lung regions-left lateral and left upper posterior. A refined model that included the right upper anterior (RUA) region further improved performance. On the external test set (n = 42), the rule-based model with RUA achieved the highest accuracy (0.93) and the lowest false negative rate (0.11), outperforming the 6-region model. Adding more regions did not enhance accuracy. CONCLUSIONS: A simplified, rule-based model that accounts for the differential predictive value of individual lung regions may enhance the accuracy of LUS-based prediction of surfactant need in preterm infants. It is also more accessible, effective, and time-efficient for clinicians.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".