The Effect of Smoking on Early Bone Remodeling on Surface Modified Southern Implants®
Bibliographic record
Abstract
INTRODUCTION: Smoking affects the survival of turned titanium implants. Although smoking has less impact on the failure rate of rough surface implants, the effect on bone loss on rough surface implants has not been studied yet and may be an important factor in biological stability. AIM: To determine the effect of smoking on early implant failures and bone remodeling around moderately rough implants (Southern Implants®, Southern Implants, Irene, South Africa). MATERIALS AND METHODS: Three hundred twenty-nine patient records, containing information on 712 installed implants, were scrutinized retrospectively and periapical radiographs were analyzed for interproximal bone level. Mann-Whitney U-test and Fisher's exact test were performed to compare bone level and implant survival in smokers and nonsmokers. Only implants with at least 6 months of function time were analyzed for bone level changes. RESULTS: The overall survival rate was 98.3%. Implants in smokers had a threefold higher failure rate compared with nonsmokers (5/104 = 4.8% vs 7/608 = 1.2%). This was statistically significant on implant level (p = .007) but not on patient level (1/41 vs 7/288, p = .997). Readable radiographs from 363 implants in 169 patients were available with a mean follow-up of 12 months (SD 5.11; range 6-28). The mean interproximal bone level was 1.36 mm (n = 363; SD 0.41; range 0.48-3.70). Bone levels were independent of jaw location. Sixty implants from 21 smokers lost statistically significantly (p = .001) more bone (mean 1.56; SD 0.53; range 0.75-3.22) than the 303 implants in 148 nonsmokers (mean 1.32 mm; SD 0.38; range 0.48-3.7). The maxilla is especially prone to bone loss compared with the mandible (1.70 mm vs 1.26 mm, p < .001). CONCLUSION: The Southern Implants® system demonstrated a high absolute survival rate. Although smokers are not more prone to implant loss, more pronounced peri-implant bone loss was observed, especially in the maxilla. Whether this affects future biological complications remains to be investigated in prospective long-term studies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".