Effects of smoking on tooth loss among individuals under periodontal maintenance therapy: a systematic review and meta-analysis
Bibliographic record
Abstract
Dental mortality has been reported by longitudinal studies on periodontal maintenance therapy (PMT), but the independent effect of smoking on tooth loss (TL), adjusted for important confounding variables, has been poorly evaluated. This systematic review aimed to assess and analyze the isolated effect of smoking TL among individuals undergoing PMT. Electronic, manual, grey literature, and recent articles (from April 2018) were searched, with no restriction regarding language; respective dates of publication were included. Epidemiological clinical studies reporting TL data among smokers undergoing PMT in comparison to nonsmoker control groups were selected. Methodological quality was assessed using the Newcastle-Ottawa Scale. Meta-analysis was performed, as well as I2 heterogeneity and sensitivity tests. Evidence quality was assessed using GRADE (Grading of Recommendations, Assessment, Development and Evaluation). Eleven papers were included in the systematic review: four case-control and seven cohort studies. Ten out of the 11 studies concluded that smoking was an important risk factor for the occurrence of TL. Meta-analysis of four of the cohort studies found that smokers had 3.24 times the chance of occurrence of TL than nonsmokers undergoing PMT (95%CI: 1.33-7.90). Overall, studies' risk of bias was low. The quality of the scientific evidence moderately supports that smokers undergoing PMT have a greater chance of TL than nonsmokers.
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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".