Analyzing Risks of Adverse Pregnancy Outcomes
Bibliographic record
Abstract
Approaches for analyzing the risks of adverse pregnancy outcomes have been the source of much debate and many publications. Much of the problem, in our view, is the conflation of time at risk with gestational age at birth (or birth weight, a proxy for gestational age). We consider the causal questions underlying such analyses with the help of a generic directed acyclic graph. We discuss competing risks and populations at risk in the context of appropriate numerators and denominators, respectively. We summarize 3 different approaches to quantifying risks with respect to gestational age, each of which addresses a distinct etiological or prognostic question (i.e., cumulative risk, prospective risk, or instantaneous risk (hazard)) and suggest the appropriate denominators for each. We show how the gestational age-specific risk of perinatal death (PND) can be decomposed as the product of the gestational age-specific risk of birth and the risk of PND conditional on birth at a given gestational age. Finally, we demonstrate how failure to consider the first of these 2 risks leads to selection bias. This selection bias creates the well-known crossover paradox, thus obviating the need to posit common causes of early birth and PND other than the study exposure.
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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 | 0.042 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".