Volumetric Evaluations of Full‐Arch Implant Supported Restorations and Their Role on Patients’ Quality of Life: A Mixed‐Model Analysis
Bibliographic record
Abstract
Introduction. Full‐arch, implant‐supported hybrid restorations, employing tilted implants, can offer an efficient way of treating edentulous patients. Several factors, such as the timing of implant placement and the inclination of the fixture, can have a detrimental effect on their stability. This retrospective study is aimed at discerning the role played by those factors. Materials and Methods. Patients treated with full‐arch, implant supported restorations were selected for this study; cone‐beam computed tomography (CBCT) images, taken 3 months and 3 years after delivery of the final restoration, as well as peri‐implant values, were obtained and compared; bone loss was measured on four sites for each implant and then averaged. These patients were recalled, and the OHIP‐5 questionnaire was administered. Results. 21 patients, with a mean age of 53 years, were included in the present analysis. 108 implants were placed, and 25 Toronto prostheses were delivered. According to a mixed‐model analysis, tilted implants (0.51, p < 0.001) had a higher rate of bone loss, while implants placed in a healed ridge suffered less bone loss than immediate implants (‐0.21, p < 0.001). Patient‐level variables have a significant effect on this variable, as implants coming from the same subject share a similar risk of bone loss. The mean response to the self‐administered OHIP‐5 questionnaire was 1.53 ± 0.29; other variables did not have a statistically significant effect on this outcome. Discussion/Conclusions. The results of the present study show that Toronto bridges prostheses are an efficient procedure for treating edentulous patients, as their oral‐related quality of life is reported as satisfactory even 7 years after delivery of the restoration. Tilted and immediate implants are more at risk of bone loss. Implants coming from the same subject share a similar risk of bone loss.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".