Placental Weight for Gestational Age and Adverse Perinatal Outcomes
Bibliographic record
Abstract
OBJECTIVE: The fetoplacental ratio has been used conventionally to study the contribution of the placenta to fetal growth restriction. However, this measure is problematic because a normal fetoplacental ratio can reflect birth weight and placental weight that are both normal, both low, or both high. The objective of this study was to examine the independent association between placental weight for gestational age and perinatal mortality or serious neonatal morbidity. METHODS: A sex- and gestational age-specific placental weight z score was calculated for a cohort of 87,600 singleton births at the Royal Victoria Hospital in Montreal, Canada, 1978-2007. The relationship between placental weight z score and adverse perinatal outcomes (stillbirth, neonatal death, 5-minute Apgar score lower than 7, seizures, or respiratory morbidity) was examined using logistic regression. Multivariable models examined whether the relationship was independent of birth weight and other pregnancy risk factors. RESULTS: : After controlling for birth weight, fetuses with a low placental weight z score were at significantly increased risk of stillbirth (odds ratio [OR] 2.0, 95% confidence interval [CI] 1.4-2.6, percent population attributable risk 17.8%). In contrast, adverse neonatal outcomes were significantly more likely among those with high placental weight z scores (OR 1.4, 95% CI 1.2-1.7, percent population attributable risk 5% for any serious neonatal morbidity). Similar trends were observed after further adjusting for pregnancy risk factors. CONCLUSION: Placental weight for gestational age is an independent risk factor for adverse perinatal outcomes, above and beyond the known association with birth weight. The mechanisms behind the opposing effects of placental weight z score on risk of stillbirth compared with adverse neonatal outcomes require further elucidation. LEVEL OF EVIDENCE: III.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".