Re: "Analyzing Risks of Adverse Pregnancy Outcomes"
Bibliographic record
Abstract
In a recent article, Kramer et al. (1) addressed the “gestational age paradox,” in which an exposure that exerts a harmful influence overall appears protective among babies born preterm. The authors illustrated this phenomenon by comparing the perinatal mortality of singletons with that of twins. They proposed that the observed reversal of comparative risk across the gestational age range is due to a type of selection bias (1). However, their proposed selection bias requires more evidence to be convincing. We previously suggested a different possible mechanism for this paradox (2, 3). Using simple scenarios, we posited unmeasured factors that could cause both early birth and neonatal death. Such factors would make gestational age at birth a “collider” in the analysis of factors (such as twinning) that also cause early birth. The unmeasured factors would produce a form of selection bias (4), in that babies born early are not a random sample of the population of fetuses. This bias distorts analyses that condition on gestational age at birth. We show explicitly in our scenarios how unmeasured factors can make a harmful exposure appear protective at early gestational ages (2, 3). Kramer et al. regard our assumption of unmeasured factors as unnecessarily speculative. They prefer their explanation as more parsimonious and thus more plausible. Perhaps they are right—but their own explanation lacks an explicit demonstration. The authors argue indirectly by analogy and by broad resolution of the paradox through fetuses-at-risk analysis. Can they show directly, as we did—in a simple numerical example with realistic parameters—the precise mechanism of the selection bias they propose, and how that mechanism could produce the empirical paradox? This research was supported by the Intramural Research Program of the National Institutes of Health, National Institute of Environmental Health Sciences. Conflict of interest: none declared.
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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 | 0.028 | 0.117 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.022 | 0.012 |
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".