Technique to Obtain a Predictable Aesthetic Result through Appropriate Placement of the Prosthesis/Soft Tissue Junction in the Edentulous Patient with a Gingival Smile
Bibliographic record
Abstract
BACKGROUND: Treating the edentulous patient with a gingival smile requires securing the prosthesis/soft tissue junction (PSTJ) under the upper lip. PURPOSE: To present a simple method that helps achieve a predictable aesthetic result when alveoplasty of the anterior maxilla is needed to place implants apical to the presurgical position of the alveolar ridge. MATERIALS AND METHODS: The maximum smile line of the patient is recorded and carved on a thin silicone bite impression as a soft tissue landmark. During the three-dimensional radiographic examination, the patient wears the silicone guide loaded with radiopaque markers. The NobelClinician® software is then used to bring the hard and soft tissue landmarks together in a single reading. Using the software, a line is drawn 5 mm apical to the smile line; it dictates the position of the crestal ridge to be reached following the alveoplasty. Subsequently, the simulated implant position and the simulated residual bone height following alveoplasty can be simultaneously evaluated on each transverse section. RESULTS: An alveoplasty of the anterior maxilla was performed as simulated on the software, and implants were placed accordingly. The PSTJ was always under the upper lip, even during maximum smile events. The aesthetic result was, therefore, fully satisfactory. CONCLUSION: This simple method permits the placement of the PSTJ under the upper lip with a predictable outcome; it ensures a reliable aesthetic result for the edentulous patient with a gingival smile.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".