Periodontal disease and perinatal outcomes: a case‐control study
Bibliographic record
Abstract
AIM: Our aim was to measure the association of maternal periodontitis with low birth weight (LBW), pre-term LBW, and intra-uterine growth restriction. MATERIAL AND METHODS: An inclusive case-control design including subjects examined for periodontitis through attachment loss, information on perinatal outcomes and general health. Data were analysed through conditional logistic regression. RESULTS: Cases (n=304) and controls (n=611) had similar prevalence and severity of periodontitis, defined as at least three sites, in different teeth, with loss of three or more millimetres of clinical attachment level. Several factors were associated with the outcome, but the crude odds ratio for periodontitis was not significant. Odds ratio were 0.93 [95% confidence interval (CI): 0.63-1.41] for LBW and 0.92 (95% CI:0.54-1.57) for pre-term LBW in the presence of periodontitis, after adjustment for maternal age, previous pregnancies, pre-natal care, smoking, previous low birth or premature birth and other medical conditions, on a hierarchical model. CONCLUSIONS: Results do not support the hypothesis of association observed in previous studies after appropriate controlling for confounding variables. Negative peri-natal outcomes are better explained by determinants other than periodontal health. This study adds to the growing body of literature on the relationship between periodontal diseases and systemic health.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".