Reconciling the High Rates of Preterm and Postterm Birth in the United States
Bibliographic record
Abstract
OBJECTIVE: Preterm and postterm birth rates are substantially higher in the United States than in Canada and other industrialized countries, although relative mortality at preterm compared with term gestation is considerably lower. We attempted to explain these differences based on differences in the method of gestational age estimation. METHODS: We used information on all live births in the United States and Canada for 1995-2002 and on singleton births and perinatal deaths for 1996-1999. Gestational age in Canada was based on the clinical estimate, whereas in the United States both menstrual-based and clinical estimates were used. RESULTS: In 2002, preterm (12.3%) and postterm birth (6.6%) rates in the United States were far higher than in Canada (7.6% and 1.0%, respectively) when U.S. rates were based on menstrual dates. Differences were reduced or abolished when U.S. rates were based on the clinical estimate of gestation (10.1% and 1.0%, respectively). In Canada, the rate ratio for perinatal death at preterm compared with term gestation was 27.8 (95% confidence interval [CI] 26.3-29.3), similar to that in the United States when gestation was based on the clinical estimate (rate ratio 26.5, 95% CI 26.1-26.9, P value for difference in rate ratios=.06) but not when based on menstrual dates (rate ratio 18.9, 95% CI 18.7-19.2, P<.001). CONCLUSION: Menstrual dates in U.S. data misclassify gestational duration and overestimate both preterm and postterm birth rates. For international comparisons, gestational age in the United States should be based on the clinical estimate. LEVEL OF EVIDENCE: II.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".