Effect of tilted and short distal implants on axial forces and bending moments in implants supporting fixed dental prostheses: an in vitro study.
Bibliographic record
Abstract
PURPOSE: The aim of this study was to evaluate the axial forces (AFs) and bending moments (BMs) on implants supporting a fixed dental prosthesis (FDP) with a distal cantilever (10 mm) compared to an FDP supported by a tilted or short (7 mm instead of 13 mm) posterior implant by means of in vitro strain gauge measurements. MATERIALS AND METHODS: Nine titanium Branemark implants were placed in an edentulous composite mandible. The mechanical loading conditions were evaluated for the following three situations: (1) short distal implants supporting a cantilever, (2) long tilted distal implants, and (3) no distal implants supporting a cantilever. A vertical load of 50 N was applied at the first molar position, and the resultant AFs and BMs were measured for the three different situations, three different numbers of supporting implants (three, four, or five), and three different prosthesis materials (titanium, acrylic, and fiber-reinforced acrylic). RESULTS: The mean BMs, as well as the maximum AFs and BMs, were significantly higher in the model with a cantilever compared to that having the tilted or short distal implants (P < .001). There was no significant difference between the models with a distally tilted implant versus a short distal implant. CONCLUSION: The use of posterior implants reduced the AFs and BMs on implants supporting an FDP compared to that with a distal cantilever. No difference in mechanical loading was observed between short tilted distal implants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".