Preconception Periodontitis and Risk of Spontaneous Abortion in a Prospective Cohort Study
Bibliographic record
Abstract
Few studies have evaluated the association between periodontitis and spontaneous abortion (SAB), and all had limitations. We used data from the Pregnancy Study Online (PRESTO), a prospective preconception cohort study of 3,444 pregnancy planners in the United States and Canada (2019-2022), to address this question. Participants provided self-reported data on periodontitis diagnosis, treatment, and symptoms of severity (i.e., loose teeth) via the enrollment questionnaire. SAB (pregnancy loss at <20 weeks' gestation) was assessed via bimonthly follow-up questionnaires. Participants contributed person-time from the date of a positive pregnancy test to the gestational week of SAB, loss to follow-up, or 20 weeks' gestation, whichever came first. We fitted Cox regression models with weeks of gestation as the time scale to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs), and we used inverse probability of treatment weighting to account for differential loss to follow-up. We used probabilistic quantitative bias analysis to estimate the magnitude and direction of the effect of exposure misclassification bias on results. In weighted multivariable models, we saw no appreciable association between preconception periodontitis diagnosis (HR = 0.97, 95% CI: 0.76, 1.23) or treatment (HR = 1.01, 95% CI: 0.79, 1.27) and SAB. A history of loose teeth was positively associated with SAB (HR = 1.38, 95% CI: 0.88, 2.14). Quantitative bias analysis indicated that our findings were biased towards the null but with considerable uncertainty in the bias-adjusted results.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".