The Preconception Period analysis of Risks and Exposures Influencing health and Development (PrePARED) consortium
Bibliographic record
Abstract
BACKGROUND: Preconception health may have intergenerational influences. We have formed the PrePARED (Preconception Period Analysis of Risks and Exposures influencing health and Development) research consortium to address methodological, conceptual, and generalisability gaps in the literature. OBJECTIVES: The consortium will investigate the effects of preconception exposures on four sets of outcomes: (1) fertility and miscarriage; (2) pregnancy-related conditions; (3) perinatal and child health; and (4) adult health outcomes. POPULATION: A study is eligible if it has data measured for at least one preconception time point, has a minimum of selected core data, and is open to collaboration and data harmonisation. DESIGN: The included studies are a mix of studies following women or couples intending to conceive, general-health cohorts that cover the reproductive years, and pregnancy/child cohort studies that have been linked with preconception data. The majority of the participating studies are prospective cohorts, but a few are clinical trials or record linkages. METHODS: Data analysis will begin with harmonisation of data collected across cohorts. Initial areas of interest include nutrition and obesity; tobacco, marijuana, and other substance use; and cardiovascular risk factors. PRELIMINARY RESULTS: Twenty-three cohorts with data on almost 200 000 women have combined to form this consortium, begun in 2018. Twelve studies are of women or couples actively planning pregnancy, and six are general-population cohorts that cover the reproductive years; the remainder have some other design. The primary focus for four was cardiovascular health, eight was fertility, one was environmental exposures, three was child health, and the remainder general women's health. Among other cohorts assessed for inclusion, the most common reason for ineligibility was lack of prospectively collected preconception data. CONCLUSIONS: The consortium will serve as a resource for research in many subject areas related to preconception health, with implications for science, practice, and policy.
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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 | 0.117 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".