Adverse Infant Outcomes Associated with Discordant Gestational Age Estimates
Bibliographic record
Abstract
BACKGROUND: Gestational age estimation by last menstrual period (LMP) vs. ultrasound (or best obstetric estimate in the US) may result in discrepant classification of preterm vs. term birth. We investigated whether such discrepancies are associated with adverse infant outcomes. METHODS: We studied singleton livebirths in the Medical Birth Registries of Norway, Sweden and Finland and US live birth certificates from 1999 to the most recent year available. Risk ratios (RR) with 95% confidence intervals (CI) by discordant and concordant gestational age estimation for infant, neonatal and post-neonatal mortality, Apgar score <4 and <7 at 5 min, and neonatal intensive care unit (NICU) admission were estimated using generalised linear models, adjusting for maternal age, education, parity, year of birth, and infant sex. Results were presented stratified by country. RESULTS: Compared to infants born at term by both methods, infants born preterm by ultrasound/best obstetric estimate but term by LMP had higher infant mortality risks (range of adjusted RRs 3.9 to 7.2) and modestly higher risks were obtained among infants born preterm by LMP but term by ultrasound/best obstetric estimate (range of adjusted RRs 1.6 to 1.9). Risk estimates for the other outcomes showed the same pattern. These findings were consistent across all four countries. CONCLUSIONS: Infants classified as preterm by ultrasound/best estimate, but term by LMP have consistently higher risks of adverse outcomes than those classified as preterm by LMP but term by ultrasound/best estimate. Compared with ultrasound/best estimate, use of LMP overestimates the proportion of births that are preterm.
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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 | 0.013 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".