The Impact of Smoking on Dental Implant Success, Peri-Implantitis, and Marginal Bone Loss: A Systematic Review
Bibliographic record
Abstract
Background: Dental implants are a reliable and widely used approach for tooth replacement. However, their long-term success can be influenced by several biological and behavioral risk factors. Among these, smoking represents a major modifiable factor known to interfere with osseointegration, wound healing, and peri-implant tissue integrity. This systematic review aimed to evaluate the influence of smoking on dental implant success rates, peri-implantitis occurrence, and long-term implant stability in adult patients. Methods: A comprehensive search was conducted across PubMed, Scopus, and Cochrane Library databases for studies published up to [insert month, year]. Clinical and observational studies comparing smokers and non-smokers receiving dental implants were included. The primary outcomes were implant failure rate, peri-implantitis occurrence, and marginal bone loss. Data were extracted and synthesized narratively due to heterogeneity in study design and reporting. Risk of bias was evaluated using the Newcastle–Ottawa Scale for observational studies. Results: Twenty-five studies met the inclusion criteria. Implant failure was significantly higher among smokers (8.9–10.2%) than non-smokers (3.7–4.5%), with reported odds ratios above 2.0. The prevalence of peri-implantitis was also greater in smokers (30.5–37.6%) compared with non-smokers (16.4–18.4%), corresponding to risk ratios up to 2.79. Smokers exhibited additional marginal bone loss of approximately 0.33–0.51 mm. Conclusion: Smoking substantially increases the risk of implant failure, peri-implantitis, and marginal bone loss, thereby reducing implant longevity. Smoking status should be considered a critical factor in preoperative assessment, treatment planning, and patient education to enhance implant success. The review included heterogeneous observational data with variability in follow-up duration, diagnostic criteria, and reporting standards, which limited the ability to perform a meta-analysis.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".