<scp>PROspective</scp> Meta‐analysis Of Trials of Initial Oxygen in preterm Newborns (<scp>PROMOTION</scp>): Protocol for a systematic review and prospective meta‐analysis with individual participant data on initial oxygen concentration for resuscitation of preterm infants
Bibliographic record
Abstract
BACKGROUND: Clinicians favour low oxygen concentrations when resuscitating preterm infants immediately after birth despite inconclusive evidence to support this practice. Prospective meta-analysis (PMA) is a novel approach where studies are identified as eligible for inclusion in the meta-analysis before their results are known. AIMS: To explore whether high (60%) or low (30%) oxygen is associated with greater efficacy and safety for the initial resuscitation (immediately after birth) of preterm infants born at <29 weeks' gestation. METHODS: We will conduct a prospective meta-analysis (PMA) with individual participant data (IPD). We will perform a systematic search to identify ongoing RCTs including infants <29 weeks' gestation randomised to high (60%) or low (30%) oxygen for initial resuscitation after birth. IPD will be sought for all infants randomised for the purpose of meta-analysis. We will employ a one-stage random-effects approach to IPD meta-analysis. Potential heterogeneity and the differential effect of high or low oxygen will be explored through subgroup and interaction analyses. The primary outcome of this study is all-cause mortality prior to hospital discharge. There will be a follow-up analysis of neurodevelopmental outcomes once available. RESULTS/CONCLUSION: The results of neonatal outcomes at hospital discharge are expected by 2025, and neurodevelopmental outcomes by 2027.
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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 | 0.037 | 0.112 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.062 | 0.005 |
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".