Methodological Challenges in International Comparisons of Perinatal Mortality
Bibliographic record
Abstract
PURPOSE OF REVIEW: Several prestigious agencies routinely rank countries based on crude perinatal and infant mortality rates, while more recently, international neonatal networks have begun comparing neonatal mortality and morbidity rates among very preterm and very low-birth-weight infants. We discuss the methodologic challenges that compromise such comparisons and potential remedies. RECENT FINDINGS: Crude perinatal mortality rates are biased by international variations in birth registration, especially at the borderline of viability. Such bias is demonstrated by significant differences in crude versus birth weight- and gestational age-specific comparisons of perinatal mortality. Comparisons of neonatal mortality among very preterm and very low-birth-weight infants are plagued by incorrect denominators, and this leads to paradoxical findings. SUMMARY: A lack of standardization with regard to birth registration and inadequate appreciation of the methods for calculating gestational age-specific mortality rates are responsible for biasing international comparisons of perinatal mortality.
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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 | 0.148 | 0.362 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".