Influence of Prosthesis Material on the Loading of Implants That Support a Fixed Partial Prosthesis: In Vivo Study
Bibliographic record
Abstract
BACKGROUND: Since loading is increasingly believed to be a determining factor in the treatment outcome with oral implants, there is a need to expand the knowledge related to the biomechanics of oral implants and its influencing factors. PURPOSE: The aim of this study was to investigate the influence of prosthesis material on the distribution and magnitude of load on oral implants carrying a fixed partial prosthesis by in vivo quantification and qualification of this load. METHODS: Eight patients with in total nine three-unit fixed partial prostheses on three implants and three patients with in total four two-unit fixed partial prostheses on two implants were selected. Both metal and acrylic resin prostheses were made. Strain gauged abutments were used to measure the load on the supporting implants during controlled load application of 50 N on several positions along the occlusal surface of the prostheses and during maximal biting in maximal occlusion. Additional tests were conducted when the three-unit prostheses were supported only by two implants, thereby creating an extension pontic. RESULTS: A significantly better distribution of bending moments with the metal prostheses in comparison to the acrylic resin prostheses was observed in the case of the three-unit prostheses on two implants. No other difference in load or load distribution with the different prosthesis materials was noted. CONCLUSION: The clinical significance of the study reveals an increased risk for bending overload of the implants that are closest to the point of load application only in the case of acrylic resin long span prostheses or acrylic resin prostheses with extensions.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".