Impact of implant number, distribution and prosthesis material on loading on implants supporting fixed prostheses
Bibliographic record
Abstract
The purpose of this study is to evaluate axial forces and bending moments (BMs) on implants supporting a complete arch fixed implant supported prosthesis with respect to number and distribution of the implants and type of prosthesis material. Seven oral Brånemark implants with a diameter of 3.75 mm and a length of 13 and 7 mm (short distal implant) were placed in an edentulous composite mandible used as the experimental model. One all-acrylic, one fibre-reinforced acrylic, and one milled titanium framework prosthesis were made. A 50 N vertical load was applied on the extension 10 mm distal from the most posterior implant. Axial forces and BMs were measured by calculating signals from three strain gauges attached to each of the abutments. The load was measured using three different models with varying numbers of supporting implants (3, 4 and 5), three models with different implant distribution conditions (small, medium and large) and three models with different prosthesis materials (titanium, acrylic and fibre-reinforced acrylic). Maximum BMs were highest when prostheses were supported by three implants compared to four and five implants (P < 0.001). The BMs were significantly influenced by the implant distribution, in that the smallest distribution induced the highest BMs (P < 0.001). Maximum BMs were lowest with the titanium prosthesis (P < 0.01). The resultant forces on implants were significantly associated with the implant number and distribution and the prosthesis material.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".