Micromotion analysis of different implant configuration, bone density, and crestal cortical bone thickness in immediately loaded mandibular full‐arch implant restorations: A nonlinear finite element study
Bibliographic record
Abstract
BACKGROUND: Excessive micromotion may cause failure of osseointegration between the implant and bone. PURPOSE: This study investigated the effects of implant configuration, bone density, and crestal cortical bone thickness on micromotion in immediately loaded mandibular full-arch implant restorations. MATERIALS AND METHODS: A finite element model of the edentulous mandible was constructed. Four implants were inserted in two different configurations, which were four parallel implants or tilted distal implants according to the all-on-four concept. Different cancellous bone densities and crestal cortical bone thicknesses were simulated. The framework was made of acrylic resin. A vertical load of 200 N was applied at the cantilever or on the distal implant (noncantilever loading). RESULTS: The maximum extent of micromotion was significantly influenced by the density of cancellous bone and to a lesser extent by implant configuration and the crestal cortical bone thickness. The all-on-four configuration showed less micromotion than the parallel implant configuration in some circumstances. The maximum micromotion detected with noncantilever loading was less than 1/3 of that with cantilever loading. CONCLUSIONS: Implant configuration had a limited influence on micromotion. Avoiding cantilever loading during the healing period should effectively reduce the risk of excessive micromotion in patients with low-density cancellous bone and thin crestal cortical bone.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".