Effect of Smoking Habits on Accuracy of Implant Placement Using Mucosally Supported Stereolithographic Surgical Guides
Bibliographic record
Abstract
BACKGROUND: Smoking is considered as a factor for implant survival and peri-implant bone loss of dental implants. Several studies revealed the negative effect of smoking on osseointegration and its dose-related effect. PURPOSE: To evaluate the effect of smoking habits on accuracy of implant placement using mucosally supported stereolithographic surgical guides. MATERIAL AND METHODS: Six OsseoSpeed™ implants (Astra Tech AB, Mölndal, Sweden) were inserted into the maxilla in 13 patients. Patients were excluded if they suffered from any systemic disease or if they were actually taking any kind of medication. Software (Mimics® 9.0) was used to fuse images of the virtually planned and actually placed implants, and locations and axes were compared between the nonsmoking and smoking subgroups. As the mucosal biotype could probably influence accuracy data, 12 reference points were defined within each patient to define a mean mucosal thickness value. RESULTS: In the smoking subgroup, 36 implants were placed compared with 42 in the nonsmoking subgroup. Mean coronal deviation was 1.04mm (range: 0.29-2.45mm) among the smokers compared with 0.80mm among the nonsmokers (range: 0.29-1.67mm). At apical point, mean deviation was 1.26mm (range: 0.39-3.01mm) among the smokers compared with 1.02mm among the nonsmokers (range: 0.32-2.59mm). Mean angular deviation was 2.64° (range: 0.41-6.81°) among the smokers compared with 2.57° among the nonsmokers (range: 0.16°-8.86°). Significant differences were found when comparing global coronal and apical deviation between the smokers and the nonsmokers (p<.05). Evaluating mucosal thickness, mean value was 3.19mm (range: 2.39-4.01mm) among the smokers compared with 2.43mm among the nonsmokers (range: 1.44-3.03mm). CONCLUSIONS: Statistically significant differences were found when comparing the accuracy of dental implant placement of the smokers with the nonsmokers. Smokers have significant thicker supporting mucosal tissues compared with nonsmokers, which may explain inaccuracy due to less stability of the surgical guide or the scanning prosthesis.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".