NeOProM: Neonatal Oxygenation Prospective Meta-analysis Collaboration study protocol
Bibliographic record
Abstract
BACKGROUND: The appropriate level of oxygenation for extremely preterm neonates (<28 weeks' gestation) to maximise the greatest chance of survival, without incurring significant morbidity, remains unknown. Infants exposed to lower levels of oxygen (targeting oxygen saturations of <90%) in the first weeks of life are at increased risk of death, cerebral palsy, patent ductus arteriosus, pulmonary vascular resistance and apnoea, whilst those maintained in higher levels of oxygen (targeting oxygen saturations of >90%) have been reported to have greater rates of morbidity including retinopathy of prematurity and chronic lung disease. In order to answer this clinical dilemma reliably, large scale trial evidence is needed. METHODS/DESIGN: To detect a small but important 4% increase in death or severe disability in survivors, over 5000 neonates would need to be recruited. As extreme prematurity affects 1% of births, such a project undertaken by one trial group would be prohibitively lengthy and expensive. Hence, the Neonatal Oxygenation Prospective Meta-analysis (NeOProM) Collaboration has been formed. A prospective meta-analysis (PMA) is one where studies are identified, evaluated, and determined to be eligible before the results of any included studies are known or published, thereby avoiding some of the potential biases inherent in standard, retrospective meta-analyses. This methodology provides the same strengths as a single large-scale multicentre randomised study whilst allowing greater pragmatic flexibility. The NeOProM Collaboration protocol (NCT01124331) has been agreed prior to the results of individual trials being available. This includes pre-specifying the hypotheses, inclusion criteria and outcome measures to be used. Each trial will first publish their respective results as they become available and the combined meta-analytic results, using individual patient data, will be published when all trials are complete. The primary outcome to be assessed is a composite outcome of death or major disability at 18 months - 2 years corrected age. Secondary outcomes include several measures of neonatal morbidity. The size of the combined dataset will allow the effect of the interventions to be explored more reliably with respect to pre-specified patient- and intervention-level characteristics. DISCUSSION: Results should be available by 2014.
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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 | 0.064 | 0.122 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.092 | 0.012 |
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".