Data Collection Variability Across Neonatal Hypoxic-Ischemic Encephalopathy Registries
Bibliographic record
Abstract
OBJECTIVE: To assess variability among data elements collected among existing neonatal hypoxic-ischemic encephalopathy (HIE) data registries worldwide and to determine the need for future harmonization of standard common data elements. STUDY DESIGN: This was a cross-sectional study of data elements collected from current or recently employed HIE registry data forms. Registries were identified by literature search and email inquiries to investigators worldwide. Data elements were categorized by group consensus. RESULTS: A total of 1281 data elements were abstracted from 22 registries based in 14 countries, including 3 middle-income countries. Registries had a median of 106.5 distinct data elements per registry (range 59-458). The most commonly collected data were related to pregnancy, therapeutic hypothermia, and short-term hospital outcomes. The least consistently collected data were laboratory values other than acid/base status values. Only 4 variables were consistently collected in every registry. Five registries included neurodevelopmental follow-up fields and 5 others linked their data to a separate follow-up registry. CONCLUSION: Many HIE registries are collecting patient data around the world, but there is considerable variability in the number, type, and format of data collected. Future attempts to develop standard common data elements to harmonize data collection globally will be crucial to facilitate worldwide collaboration and to optimize management and outcome of neonatal HIE.
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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 | 0.135 | 0.246 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".