Might Rare Factors Account for Most of the Mortality of Preterm Babies?
Bibliographic record
Abstract
BACKGROUND: Preterm delivery has a variety of causes, with each of these presumably carrying its own mortality risk. To the extent that they add to the risk of mortality, the various pathologic factors triggering preterm delivery will confound the causal contribution of gestational age to mortality, inflating the observed rates of gestational-age-specific mortality. We have previously estimated that about half of the mortality of US preterm singletons may be due to unmeasured pathologies that increase mortality risk and also cause preterm birth. In this paper, we examine the impact that rare factors may have, at least in theory, on preterm mortality. METHODS: We constructed a simple model of gestational-age-specific mortality, in which we arbitrarily selected a function to represent the mortality due to immaturity alone ("baseline" risk). We then added "unmeasured" confounding factors that cause mortality and also cause preterm birth. This construct allowed us to calculate, in simple scenarios, the proportion of preterm mortality that could be caused by unmeasured confounding. RESULTS: We found that rare pathologies with moderate-to-strong effects can substantially contribute to preterm mortality. The presence of such rare factors can also produce an intersection of gestational-age-specific mortality curves when stratifying by known risk factors. CONCLUSIONS: It is possible that a few relatively rare factors may account for a large fraction of preterm mortality. The search for such factors should be a primary focus of future research on preterm delivery.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".