Modelling prior reproductive history to improve prediction of risk for very preterm birth
Bibliographic record
Abstract
In published studies of preterm birth, analyses have usually been centred on individual reproductive events and do not account for the joint distributions of these events. In particular, spontaneous and induced abortions have often been studied separately and have been variously reported as having no increased risk, increased risk or different risks for subsequent preterm birth. In order to address this inconsistency, we categorised women into mutually exclusive groups according to their reproductive history, and explored the range of risks associated with different reproductive histories and assessed similarities of risks between different pregnancy histories. The data were from a population-based case-control study, conducted in Victoria, Australia. The study recruited women giving birth between April 2002 and April 2004 from 73 maternity hospitals. Detailed reproductive histories were collected by interview a few weeks after the birth. The cases were 603 women who had had a singleton birth between 20 and less than 32 weeks gestation (very preterm births including terminations of pregnancy) and the controls were 796 randomly selected women from the population who had had a singleton birth of at least 37 completed weeks gestation. All birth outcomes were included. Unconditional logistic regression was used to assess the association of very preterm birth with type and number of prior abortions, prior preterm births and sociodemographic factors. Using the complex combinations of prior pregnancy experiences of women (including nulligravidity), we showed that a history of prior childbirth (at term) with no preterm births gave the lowest risk of very preterm birth. With this group as the reference category, odds ratios of more than two were associated with all other prior reproductive histories. There was no evidence of difference in risk between types of abortion (i.e. spontaneous or induced) although the risk increased if a prior preterm birth had also occurred. There was an increasing risk of very preterm birth associated with increasing numbers of abortions. This method of data analysis reveals consistent and similar risks for very preterm birth following spontaneous or induced abortions. The findings point to the need to explore commonalities rather than differences in regard to the impact of abortion on subsequent births.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".