Temporal Changes in Small-for-Gestational Age Live Births Associated with Obstetric Intervention in the United States.
Bibliographic record
Abstract
INTRODUCTION: Iatrogenic early delivery through labour induction/cesarean delivery given suspected fetal/maternal compromise is the foundation of modern obstetrics. It has been hypothesized that increasing rates of obstetric intervention may be responsible for decreasing rates of small for gestational age (SGA) births at the population level, as fetuses that would have been delivered spontaneously at later gestations after a period of impaired growth are instead being delivered earlier, prior to the onset of SGA. METHODS: Population-based data on singleton live births born between 24–43 weeks of gestation in the United States from 1990 to 2010 were obtained. The fetuses-at-risk approach was used to calculate the gestational-age specific rates of SGA and obstetrical intervention. Kitagawa decomposition was used to assess the relative contribution of changes in the gestational age distribution and the gestational age-specific SGA rates to the overall temporal changes in SGA rates. RESULTS: The rate of SGA births declined steadily from 10.1% in 1990–92 to 8.9% in 2002–04; however, starting in 2005–07, the SGA rate slowly increased and was 9.1% in 2008–10. The changing rates of early delivery associated with obstetric intervention mirrored this change, with a 45.8% increase in labour induction/cesarean delivery between 1990–92 to 2002–04, followed by a 29.8% decrease between 2002–04 and 2008–10. The Kitagawa decomposition indicated that the initial decline in SGA rates was entirely due to changes in the gestational age distribution, whereas the increase in the later time period was due to changes in the gestational-age specific SGA rates. This change in the gestational-age specific SGA rates was only observed in the term/post-term population.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".