Bibliographic record
Abstract
We thank Wilcox et al. (1) for their comments. In their letter, Wilcox et al. requested an “explicit demonstration” of our explanation for the crossover paradox (2). Again, we do not deny that uncontrolled common causes of preterm birth and neonatal death can create bias in estimates of the association between causes of preterm birth and neonatal death. Basso and Wilcox (3) demonstrated that confounding due to the simultaneous occurrence of 2 rare factors, both of which increase neonatal mortality (one of which also causes a large increase in birth weight, while the other causes a large decrease in birth weight), can bias associations between exposures that cause preterm birth and neonatal death among preterm infants. The possibility that this hypothetical, complicated scenario can lead to an apparently protective effect of the study exposure on neonatal death does not lend it much credibility, in our view. Instead, we claim that the higher stillbirth and livebirth rates at earlier gestational ages lead to a survivorship bias. Exposed fetuses that survive to later gestational ages are a selected subset of all exposed fetuses who have not succumbed earlier in gestation. Conditioning on survival to a later gestational age ignores earlier deaths. It also removes from the denominator of the risk expression those fetuses that remain unborn at the later gestational age, who will thereby continue their selective advantage into later gestation. As we have recently pointed out (4), the same phenomenon has been observed with other known causes of preterm birth, including maternal smoking, black (vs. white) race in the United States, twin (vs. singleton) status, primiparity, and maternal short stature.
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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 | 0.009 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.154 | 0.099 |
| Insufficient payload (model declined to judge) | 0.011 | 0.011 |
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".