An International Comparison of Death Classification at 22 to 25 Weeks’ Gestational Age
Bibliographic record
Abstract
OBJECTIVES: To explore international differences in the classification of births at extremely low gestation and the subsequent impact on the calculation of survival rates. METHODS: We used national data on births at 22 to 25 weeks’ gestation from the United States (2014; n = 11 144), Canada (2009–2014; n = 5668), the United Kingdom (2014–2015; n = 2992), Norway (2010–2014; n = 409), Finland (2010–2015; n = 348), Sweden (2011–2014; n = 489), and Japan (2014–2015; n = 2288) to compare neonatal survival rates using different denominators: all births, births alive at the onset of labor, live births, live births surviving to 1 hour, and live births surviving to 24 hours. RESULTS: For births at 22 weeks’ gestation, neonatal survival rates for which we used live births as the denominator varied from 3.7% to 56.7% among the 7 countries. This variation decreased when the denominator was changed to include stillbirths (ie, all births [1.8%–22.3%] and fetuses alive at the onset of labor [3.7%–38.2%]) or exclude early deaths and limited to births surviving at least 12 hours (50.0%–77.8%). Similar trends were seen for infants born at 23 weeks’ gestation. Variation diminished considerably at 24 and 25 weeks’ gestation. CONCLUSIONS: International variation in neonatal survival rates at 22 to 23 weeks’ gestation diminished considerably when including stillbirths in the denominator, revealing the variation arises in part from differences in the proportion of births reported as live births, which itself is closely connected to the provision of active 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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".