United States Birth Weight Reference Corrected For Implausible Gestational Age Estimates
Bibliographic record
Abstract
OBJECTIVES: To provide an updated US birth weight for gestational age reference corrected for likely errors in last menstrual period (LMP)-based gestational age dating, as well as means and SDs, to enable calculation of continuous and categorical measures of birth weight for gestational age. METHODS: From the 2009-2010 US live birth files, we abstracted singleton births between 22 and 44 weeks of gestation with at least 1 nonmissing estimate of gestational age (ie, LMP or obstetric/clinical) and birth weight. Using an algorithm based on birth weight and the concordance between these gestational age estimates, implausible LMP-based gestational age estimates were either excluded or corrected by using the obstetric/clinical estimate. Gestational age- and sex-specific birth weight means, SDs, and smoothed percentiles (3rd, 5th, 10th, 90th, 95th, 97th) were calculated, and the 10th and 90th percentiles were compared with published population-based references. RESULTS: A total of 7 818 201 (99% of eligible) births were included. The LMP-based estimate of gestational age comprised 85% of the dataset, and the obstetric/clinical estimate comprised the remaining 15%. Cut points derived from the current reference identified ∼10% of births as ≤10th and ≥90th percentiles at all gestational weeks, whereas cut points derived from previous US-based references captured variable proportions of infants at these thresholds within the preterm and postterm gestational age ranges. CONCLUSIONS: This updated US-based birth weight for gestational age reference corrects for likely errors in gestational age dating and allows for the calculation of categorical and continuous measures of birth size.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".