Neonatal lung ultrasonography to evaluate need for surfactant or mechanical ventilation: a systematic review and meta-analysis
Bibliographic record
Abstract
Context Lung ultrasonography (LUS) is increasingly used to identify various neonatal respiratory disorders. There is emerging evidence that it can identify infants with significant lung disease who need surfactant treatment or mechanical ventilation. Objective To systematically review the accuracy of LUS in determining the need for surfactant treatment or mechanical ventilation in infants with respiratory distress treated with nasal continuous positive airway pressure (NCPAP). Methods Database search include EMBASE, Medline, CINAHL and Cochrane central from inception until 17 October 2018. Included is diagnostic accuracy studies reporting LUS evaluating surfactant therapy/mechanical ventilation. Two authors extracted data independently and assessed quality. Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to determine the methodological quality. Results Six studies involving 485 infants included in the review. Three studies used LUS score, two used type 1 lung profile, and one used high-risk LUS to evaluate the outcome. The pooled sensitivity and specificity at LUS score cut-off >5–6 was 88% (95% CI 80% to 93%) and 82% (95% CI 74% to 89%), respectively. Infants with LUS score >5–6 were at significantly increased risk of surfactant treatment compared with infants with LUS score <5–6 (relative risk=7.51; 95% CI 4.16 to 13.58; two studies; participants=189; I 2 =0%). The diagnostic accuracy of type 1 lung profile was better in younger preterm infants (sensitivity 88.9%, specificity 100%) compared with late preterm and term infants (sensitivity 100%, specificity 28%). Conclusions LUS, particularly LUS score, can be used accurately to determine the need for surfactant replacement treatment or mechanical ventilation in infants with respiratory distress treated with NCPAP support. The accuracy is better in younger preterm infants compared with late preterm and term infants. PROSPERO registration number CRD42018115135.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".