Smoking, a weak predictor of periodontitis in older adults
Bibliographic record
Abstract
BACKGROUND: The impact of smoking habits on periodontal conditions in older subjects is poorly studied. AIMS: To assess if a history of smoking is associated with chronic periodontitis and medical history in older subjects. MATERIAL AND METHODS: The medical and dental history was collected from 1084 subjects 60-75 years of age. Smoking history information was obtained from self-reports. Periodontal variables [clinical probing depth (PD)>/=5.0 mm, clinical attachment levels (CALs) >/=4.0 mm], and radiographic evidence of alveolar bone loss were assessed. RESULTS: 60.5% had never smoked (NS), 32.0% were former smokers (FS) (mean smoke years: 26.1 years, SD+/-13.1), and 7.5% were current smokers (CS) (mean smoke years 38.0 years, (SD+/-12.1). The proportional distribution of CAL >/=4.0 mm differed significantly by smoking status (NS and CS groups) (mean difference: 12.1%, 95% confidence interval (CI): 1.5-22.6, p<0.02). The Mantel-Haenszel common odds ratio between smoking status (CS+FS) and periodontitis (>20% bone loss) was 1.3 (p<0.09, 95% CI: 0.9-2.0) and changed to 1.8 (p<0.02, 95% CI: 1.3-2.7) with 30 years of smoking as cutoff. A weak correlation between number of years of smoking and CAL>/=4.0 mm was demonstrated (r(2) values 0.05 and 0.07) for FS and CS, respectively. Binary logistic forward (Wald) regression analysis demonstrated that the evidence of carotid calcification, current smoking status, gender (male), and the number of remaining teeth were explanatory to alveolar bone loss. CONCLUSIONS: A clinically significant impact on periodontal conditions may require 30 years of smoking or more. Tooth loss, radiographic evidence of carotid calcification, current smoking status, and male gender can predictably be associated with alveolar bone loss in older subjects.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".