Left Truncation Bias as a Potential Explanation for the Protective Effect of Smoking on Preeclampsia
Bibliographic record
Abstract
BACKGROUND: We carried out a study to examine whether left truncation bias could explain the negative association between smoking and preeclampsia. METHODS: Monte Carlo and other simulation models were used to determine the effect of differential rates of early pregnancy loss among smokers on the relation between smoking and preeclampsia at ≥20 weeks' gestation. Assumptions included no association between smoking and the abnormal placentation that characterizes preeclampsia, and higher rates of early pregnancy loss among smokers, pregnancies with abnormal placentation, and smokers with abnormal placentation. RESULTS: Monte Carlo simulation yielded a rate ratio for preeclampsia, given smoking of 0.85 (95% confidence interval = 0.73, 0.98). The protective effect of smoking was also evident in simulations that did not require assumptions about early pregnancy loss rates. CONCLUSION: Left truncation bias due to differential rates of early pregnancy loss among smokers is a plausible explanation for the inverse association between maternal smoking and preeclampsia.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".