Single tooth implants in the esthetic zone following a two‐stage all flapless approach: A retrospective analysis
Bibliographic record
Abstract
BACKGROUND: Due to chronic inflammation or trauma facial bone is frequently missing after tooth loss in the esthetic zone. As a consequence, procedures to augment or at least to preserve bone are frequently necessary prior to implant placement. PURPOSE: The aim of this retrospective case series is to demonstrate the applicability of a staged all-flapless concept to establish satisfactory implant restorations following situations of partial missing facial bone in the esthetic zone. MATERIALS AND METHODS: Radiological/clinical data of 25 patients were analyzed and an esthetic evaluation of 24 patients was performed. The staged concept included ridge preservation at time of tooth extraction and delayed guided implant placement. Marginal bone loss was measured radiologically and esthetic evaluation was performed based on standardized photographs using the Pink Esthetic Score as well as the Papilla Index. RESULTS: Implant success rate revealed 100%. The mean radiological peri-implant marginal bone loss measured 1.16 mm (SD: 0.16). Regarding the esthetic outcome 71% of patients were evaluated with a Pink Esthetic Score higher or equal to 10 constituting satisfactory esthetics (median pink esthetic score: 10). The mean follow-up time for clinical and radiographic analysis was 1.3 years (SD: 0.6 years) and 1.2 years (SD: 0.6) for esthetic evaluation. CONCLUSION: Although marginal bone loss cannot be avoided, the staged concept of flapless ridge preservation and subsequent delayed flapless guided implant placement carries the potential to improve esthetics of single-tooth implants in the anterior maxilla.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".