Determinants and consequences of discrepancies in menstrual and ultrasonographic gestational age estimates
Bibliographic record
Abstract
OBJECTIVES: To assess the association between maternal and fetal characteristics and discrepancy between last normal menstrual period and early (<20 weeks) ultrasound-based gestational age and the association between discrepancies and pregnancy outcomes. DESIGN: Hospital-based cohort study. SETTING: Montreal, Canada. SAMPLE: A total of 46,514 women with both menstrual- and early ultrasound-based gestational age estimates. MAIN OUTCOME MEASURES: Positive (last normal menstrual period > early ultrasound, i.e. menstrual-based gestational age is higher than early ultrasound-based gestational age, so that the expected date of delivery is earlier with the menstrual-based gestational age) discrepancies > or =+7 days, mean birthweight, low birthweight, stillbirth and in-hospital neonatal death. RESULTS: Multiparous mothers and those with diabetes, small stature or high pre-pregnancy body mass index were more likely to have positive discrepancies. The proportion of women with discrepancies > or =+7 days was significantly higher among chromosomally malformed and female fetuses. The mean birthweight declined with increasingly positive differences. The risk of low birthweight was significantly higher for positive differences. Associations with fetal growth measures were more plausible with early ultrasound estimates. CONCLUSIONS: Although most discrepancies between last normal menstrual period- and early ultrasound-based gestational age are attributable to errors in menstrual dating, our results suggest that some positive differences reflect early growth restriction.
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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 | 0.004 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".