The association between temporal changes in the use of obstetrical intervention and small- for-gestational age live births
Bibliographic record
Abstract
Background: The literature attributes secular declines in small-for-gestational age (SGA) live births to changes in maternal smoking and other maternal characteristics. However, there are reasons to believe that the observed reductions in SGA may be a consequence of early delivery following obstetric intervention. Methods: We examined temporal trends in obstetrical intervention and SGA among singleton live births in the United States from 1990 to 2010. The modified Kitagawa decomposition, based on the fetuses-at-risk approach, was used to assess the relative contribution of changes in the gestational age distribution and gestational age-specific SGA to overall changes in SGA. Reductions in SGA rates due to a left shift in the gestational age distribution were assumed to primarily reflect increased obstetrical intervention, whereas decreases in overall SGA due to decreases in gestational-age-specific SGA rates were assumed to reflect declines in risk factors. Results: Temporal trends in SGA followed a non-linear pattern, with substantial declines from 10.1 % in 1990–92 to 8.9 % in 2002–04, followed by a small increase to 9.1 % in 2008–10. Rates of maternal smoking steadily decreased throughout the same time period and changes in SGA rates were more consistent with changes in the gestational age distribution. The modified Kitagawa decomposition analysis also attributed the initial decline in SGA rates to changes in the gestational age distribution. Conclusions: Complex temporal pattern in SGA rates cannot be explained by the linear pattern of changes in factors like maternal smoking. Changes in the gestational age distribution are more consistent with the observed secular trends in SGA rates.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".