Nobel Perfect™ Esthetic Scalloped Implant: Rationale for a New Design
Bibliographic record
Abstract
BACKGROUND: Over the past 20 years, there have been relatively few changes in implant designs. Most systems are manufactured from commercially pure titanium with turned threads, are sprayed with plasma, are coated with hydroxyapatite, or have an oxide surface. The majority of dental implants have not been designed for differing bone morphologies. Today patients have high esthetic demands that require modifications of implant designs to fulfill their expectations. PURPOSE: This article evaluates the problems encountered when trying to achieve an optimum esthetic outcome with dental implants. Implants and abutment designs, biologic width, ridge anatomy, and timing of implant placement all affect esthetic results. Each of these factors is discussed and is related to the introduction of a new scalloped implant design. The purpose of the scalloped design is to keep or create interdental bony peaks that support the soft tissue, thereby maintaining or creating interimplant papillae. METHODS: Clinical documentation of patients treated with the scalloped implant is presented. The esthetic outcome can be determined by comparing clinical documentation prior to and after treatment. CONCLUSIONS: The scalloped implant provides clinicians and patients with the option of improving esthetic outcomes. Placement and restoration of this implant are important when planning implant treatment in the esthetic zone.
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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 | 0.004 | 0.004 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| 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".