The impact of missing birth weight in deceased versus surviving fetuses and infants in the comparison of birth weight-specific feto-infant mortality.
Bibliographic record
Abstract
Birth weight-specific is preferred to crude feto-infant mortality in epidemiologic studies comparing rates across jurisdictions, because it can help limit the bias arising from regional differences in the completeness of reporting of vital events and in classification of live versus stillbirth among extremely small and immature infants. The potential impact of missing birth weight information in deceased versus surviving fetuses and infants in the comparison of birth weight-specific feto-infant mortality has been seldom examined, however. The authors investigated this issue, using data collected from two nationwide surveys of all pregnancy outcomes occurring 15 17 May 1989 and 12 16 February 1996, respectively, in Taiwan and the 1989 and 1996 linked birth and infant death records in Canada (excluding Ontario and Newfoundland). The proportions with missing birth weight information in Taiwan in 1989 were 25.0%, 15.4%, 0%, and 0.6%, respectively, for stillbirths, neonatal deaths, post-neonatal deaths, and survivors, and in 1996 were 100%, 5.0%, 0%, and 0.2%. The proportions with missing birth weight information in Canada in 1989 were 5.8%, 2.6%, 1.2%, and 0.6%, respectively, for fetal deaths, neonatal deaths, post-neonatal deaths, and survivors, and in 1996 were 5.0%, 2.4%, 1.1%, and 0.6%. Infant and (especially) fetal death rates were substantially higher in Taiwan than in Canada among births with missing birth weight. The authors concluded that differences in missing birth weight information between deaths and survivors can bias comparisons of birth weight-specific feto-infant 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 | 0.147 | 0.285 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".