Impact of short implants numbers and prosthesis design on stress in the posterior mandible: FE analysis
Bibliographic record
Abstract
Background: This study assessed the effect of the number of short implants on stress and strain distribution in bone in the posterior mandible using finite element analysis (FEA). Materials and Methods: The study design utilized FEA, a computational technique. In FEA models, short implants (4 mm diameter and 6 mm length) were placed at the site of the mandibular first premolar to the second molar in four models: (I) two implants at the sites of teeth #4 and #7 with two pontics at the sites of teeth #5 and #6, (II) three implants at #4, #5, and #7 with one pontic at #6, (III) three implants at #4, #6, and #7 with one pontic at #5, and (IV) four implants at #4, #5, #6, and #7 with no pontic. A 100 N load was applied vertically and at a 30° angle to the occlusal surface of the crowns. Stress and strain distribution patterns in bone were evaluated using ANSYS Workbench. Results: The highest maximum von Mises and shear stress and strain values under vertical and off-axial loadings were observed in the model with two short implants at the sites of teeth #4 and #7 with two pontics at the sites of teeth #5 and #6. In general, the highest stress and strain values were recorded following the application of off-axial loads compared to vertical loads. In all models, the highest stress was noted in the cervical part of the implants, while the maximum strain occurred in the apical part of the implants. Conclusion: Increasing the number of short implants significantly reduces stress and strain values in peri-implant bone.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".