The impact of past pregnancy experience on subsequent perinatal outcomes
Bibliographic record
Abstract
In perinatal epidemiology, the basic unit of analysis has traditionally been the individual pregnancy. In this study, we sought to explore the idea of a 'reproductive life'-based approach to modelling the effects of reproductive exposures and outcomes, where the basic unit of analysis is a woman's entire reproductive experience. Our objective was to explore whether a first pregnancy risk factor, excess gestational weight gain, has a direct effect on the birthweight outcomes of a subsequent pregnancy, independent of the weight gain and other risk factors of the second pregnancy. A study population was created by linking the obstetric records of 1220 women who delivered their first and second offspring at a McGill University teaching hospital in Montreal, Canada. Multivariable linear and logistic regression analyses were used to model the effects of gestational weight gain above recommendation on the birthweight Z-score and risk of large-for-gestational age (LGA) subsequent offspring. After adjusting for the risk factors of the second pregnancy, an independent effect from the first pregnancy was seen on the birthweight Z-score, (effect size OR 0.17 [95% CI 0.05, 0.28] but not risk of LGA of the second pregnancy 1.30 [95% CI 0.89, 1.89]). We concluded that a pregnancy-centred approach to research that conceptualizes pregnancies as self-contained and interchangeable events may not always be appropriate, and propose that analytical methods for some perinatal research questions may need to consider a given pregnancy in the context of a woman's past reproductive experiences.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".