The International Network for Evaluating Outcomes (iNeo) of neonates: evolution, progress and opportunities
Bibliographic record
Abstract
Neonates born very preterm (before 32 weeks' gestational age), are a significant public health concern because of their high-risk of mortality and life-long disability. In addition, caring for very preterm neonates can be expensive, both during their initial hospitalization and their long-term cost of permanent impairments. To address these issues, national and regional neonatal networks around the world collect and analyse data from their constituents to identify trends in outcomes, and conduct benchmarking, audit and research. Improving neonatal outcomes and reducing health care costs is a global problem that can be addressed using collaborative approaches to assess practice variation between countries, conduct research and implement evidence-based practices. The International Network for Evaluating Outcomes (iNeo) of neonates was established in 2013 with the goal of improving outcomes for very preterm neonates through international collaboration and comparisons. To date, 10 national or regional population-based neonatal networks/datasets participate in iNeo collaboration. The initiative now includes data on >200,000 very preterm neonates and has conducted important epidemiological studies evaluating outcomes, variations and trends. The collaboration has also surveyed >320 neonatal units worldwide to learn about variations in practices, healthcare service delivery, and physical, environmental and manpower related factors and support services for parents. The iNeo collaboration serves as a strong international platform for Neonatal-Perinatal health services research that facilitates international data sharing, capacity building, and global efforts to improve very preterm neonate care.
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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 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".