Parametric analysis of the strength in the ''Toronto'' osseous-prosthesis system.
Bibliographic record
Abstract
AIM: The aim of this work is to analyze how the different distributions of the chewing load could be related to dental prosthesis fractures of the Toronto restoration via the fulfilment of a virtual parametric model. Moreover investigation about implant positions and perspectives in comparison with a virtual medial plane was made to evaluate the incidence of screw and prosthesis breakage. METHODS: Finite element analysis of the lower jaw was performed to underline parameters and mechanical features of dental implants connected with the fractures of the prosthetic restoration. Jaw virtual model and 3D fixtures (Global Sweden and Martina) were created by Cosmos-Solid Works. Matlab was used to recreate the distribution of an unspecific chewing phase analyzing the overall load on the fixtures of the lower jaw. RESULTS: Four virtual 3D models were performed with Matchad 14, for data analysis. The study investigated frontal and horizontal planes and vertical direction of the occlusal forces. Data results showed how position and perspective of fixtures strongly influenced the stress distribution upon the bone of the jaw; safeguard of prostheses elements like cantilever, passing screws, and dental implants are strictly related to a correct selection of the dental implant position. CONCLUSIONS: The 3D geometry of the jaw is an important condition for the choice of position, number, diameter and length of dental implants used for Toronto prostheses. This study would suggest a virtual ''method'' to help the surgeon choosing a correct model for a prosthetic rehabilitation evaluating position, perspective, and stress distribution of the chewing strengths.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".