Influence of Implant Length and Bicortical Anchorage on Implant Stress Distribution
Bibliographic record
Abstract
BACKGROUND: Short implants present superior failure rates for everybody. PURPOSE: The aim of this theoretic study was to assess to what extent implant length and bicortical anchorage affect the way stress is transferred to implant components, the implant proper, and the surrounding bone. MATERIALS AND METHODS: Stress analysis was performed using finite element analysis. A three-dimensional linear elastic model was generated. All implants modeled were of the same diameter (3.75 mm) but varied in length, at 6, 7, 8, 9, 10, 11, and 12 mm (Brånemark System, Nobel Biocare AB, Gothenburg, Sweden). Each implant was modeled with a titanium abutment screw and abutment, a gold cylinder and prosthetic screw, and a ceramic crown. The implants were seated in a supporting bone structure consisting of cortical and cancellous bone. An occlusal load of 100 N was applied at a 30 degrees angle to the buccolingual plane. RESULTS: With the selected model and bone properties, the coronal cortical anchorage was dominating, and the bone stress concentrated to that area. CONCLUSIONS: The maximum bone stress was virtually constant, independent of implant length and bicortical anchorage. The maximum implant stress, however, increased somewhat with implant length and bicortical anchorage.
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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) | 0.000 | 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) | 0.002 | 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".