Rehabilitation of the anterior edentulous maxilla with Toronto Design
Bibliographic record
Abstract
Factors such as the location of the placed implants and skeletal malocclusions may cause difficulties in designing aesthetic crowns. In this case, the goal is to design an aesthetic crown for a patient with class 3 malocclusion, despite the relatively palatal placement of the upper anterior implants. A 70-year-old male patient applied to our clinic due to missing teeth between 13 and 23. Class 3 malocclusion was determined on intraoral and radiographic examination. Following CT evaluations, four implants were placed. After a three-month waiting period, the healing cap was installed. Hybrid fixed prosthesis planning was made taking into account the condition of the ridges and the position of the implants. To meet the aesthetic expectation, the Toronto infrastructure design was decided. A personal spoon was prepared for the patient. Then, permanent measurements were taken. It was sent to the laboratory with a closing record. It was produced from chrome-cobalt (Cr-Co) alloy by the Toronto design milling method. After the metal infrastructure was adapted, gingival shaping was completed with gingival porcelain. Metal-supported crowns, produced as 6 single crowns, were rehearsed. They were subjected to the glaze process. After the Toronto substructure was screwed to the implants, the crowns were cemented in a controlled manner. With this method class 3 appearance was eliminated with tooth-to-tooth closure and the patient's aesthetic satisfaction was achieved. No problems were encountered during the 3-month patient follow-up. Toronto infrastructure system can be used as an alternative solution to aesthetic problems that we cannot overcome with traditional methods.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".