Respiratory function monitoring to improve the outcomes following neonatal resuscitation: a systematic review and meta-analysis
Bibliographic record
Abstract
IMPORTANCE: delivery might reduce injury and improve outcomes. OBJECTIVE: To determine whether use of an RFM in addition to clinical assessment versus clinical assessment alone during mask ventilation in the delivery room reduces in-hospital mortality and morbidity of infants <37 weeks' gestation. STUDY SELECTION: Randomised controlled trials (RCTs) comparing RFM in addition to clinical assessment versus clinical assessment alone during mask ventilation in the delivery room of infants born <37 weeks' gestation. DATA ANALYSIS: Risk of bias was assessed using Covidence Collaboration tool and pooled into a meta-analysis using a random-effects model. The primary outcome was death prior to discharge. MAIN OUTCOME: Death before hospital discharge. RESULTS: Three RCTs enrolling 443 infants were combined in a meta-analysis. The pooled analysis showed no difference in rates of death before discharge with an RFM versus no RFM, relative risk (RR) 95% (CI) 0.98 (0.64 to 1.48). The pooled analysis suggested a significant reduction for brain injury (a combination of intraventricular haemorrhage and periventricular leucomalacia) (RR 0.65 (0.48 to 0.89), p=0.006) and for intraventricular haemorrhage (RR 0.69 (0.50 to 0.96), p=0.03) in infants receiving positive pressure ventilation with an RFM versus no RFM. CONCLUSION: In infants <37 weeks, an RFM in addition to clinical assessment compared with clinical assessment during mask ventilation resulted in similar in-hospital mortality, significant reduction for any brain injury and intraventricular haemorrhage. Further trials are required to determine whether RFMs should be routinely available for neonatal resuscitation.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.033 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".