A Natural Experiment for Inferring Causal Association between Smoking and Tooth Loss: A Study of a Workplace Contemporary Cohort
Bibliographic record
Abstract
Background: Natural experiments in former smokers are an important criterion for inferring causality between smoking and tooth loss. We examined how former smoking influenced risk estimate of tooth loss incidence. Methods: Records of dental check-ups of the work cohort were examined. The sample consisted of data from 1,724 workers recorded at the ages of 40 years and 50 years, and this was analyzed for tooth loss incidence during a 10-year period. Former smokers were categorized into two groups based on whether they quit smoking before or during the observational period. Variables used for adjustment were age, sex, oral and overall health behavior, dental visit, and number of existing teeth immediately prior to observation. Results: The prevalence of tooth loss incidence and number of teeth lost during the observational period were both higher in current smokers than in never smokers (33.7% vs. 23.9% and 0.83 vs. 0.42, respectively). Incident odds ratio of tooth loss in long-term quitters relative to never smokers was not significant and less than one (incident odds ratio 0.85, 95% confidence interval 0.56–1.29). Incident odds ratios of short-term quitters and current smokers were both significant, though short-term quitters exhibited higher values (1.72, 1.15–2.55) than current smokers (1.48, 1.10–2.00). Conclusions: The causal interpretation is strengthened by attenuation of the risk in long-term quitters. However, additional factors may influence the risk estimates of former smokers, suggesting potential limitations of a natural experiment for inferring causal association between smoking and tooth loss.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| 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".