Predictors and Outcomes of Extubation Failure in Preterm Neonates: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Extubation failure (EF) is common in preterm neonates and may be associated with adverse outcomes. OBJECTIVE: To systematically review and meta-analyze the existing literature on predictors and outcomes of EF in preterm neonates. DATA SOURCES: MEDLINE, Epub Ahead of Print, In-Process & Other Non-Indexed Citations, Embase (OvidSP), CINAHL (EBSCOHost), and Cochrane Library (Wiley) from 1995 onward. The search strategy was developed by a reference librarian. STUDY SELECTION: Experimental or observational studies reporting on predictors and/or outcomes related to EF (defined as reintubation within 7 days) in preterm neonates less than 37 weeks were eligible. Predictors included machine learning (ML) algorithms and lung ultrasound (LUS). Main outcome of interest was association of EF with mortality and/or bronchopulmonary dysplasia (BPD). DATA EXTRACTION: Studies identified by the search strategy were screened based on title and abstract. Data from included studies were extracted independently by 2 authors, along with adjudication of risk of bias. RevMan Web was used to conduct meta-analyses. RESULTS: Out of 8336 studies screened, 120 were included. Neonates with lower gestational age at birth, birthweight, postmenstrual age, and weight at extubation were more likely to experience EF. Higher level of pre-extubation respiratory support, indicated by lower pre-extubation pH and higher pre-extubation mean airway pressure, fraction of inspired oxygen, and Pco2 were associated with EF risk. ML models showed variable accuracy and lower external validity. LUS may be a promising predictor, though scoring systems varied. EF was associated with higher odds of mortality and/or BPD (pooled odds ratio [OR], 4.7; 95% CI, 2.84-7.76) as well as the individual components of the composite: mortality (pooled OR, 3.87; 95% CI, 2.35-6.36) and BPD (pooled OR, 3.27; 95% CI, 2.54-4.21). LIMITATIONS: Associations were derived from unadjusted data, precluding a definitive causal relationship between EF and predictors/outcomes. CONCLUSIONS: Lower gestational and chronological age and higher levels of pre-extubation ventilation support were associated with EF. ML models and LUS scores require further validation in larger studies. EF was associated with mortality and/or BPD.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".