The Effects of Cortical Bone Thickness and Trabecular Bone Strength on Noninvasive Measures of the Implant Primary Stability Using Synthetic Bone Models
Bibliographic record
Abstract
PURPOSE: This study investigated how the primary stability of a dental implant as measured by the insertion torque value (ITV), Periotest value (PTV), and implant stability quotient (ISQ) is affected by varying thicknesses of cortical bone and strengths of trabecular bone using synthetic bone models. MATERIALS AND METHODS: Four synthetic cortical shells (with thicknesses of 0, 1, 2, and 3 mm) were attached to four cellular rigid polyurethane foams (with elastic moduli of 137, 47.5, 23, and 12.4 MPa) and one open-cell rigid polyurethane foam which mimic the osteoporotic bone (with an elastic modulus 6.5 MPa), to represent the jawbones with various cortical bone thicknesses and strengths of trabecular bone. A total of 60 bone specimens accompanied with implants was examined by a torque meter, Osstell resonance frequency analyzer, and Periotest electronic device. All data were statistically analyzed by two-way analysis of variance. In addition, second-order nonlinear regression was utilized to assess the correlations of the primary implant stability with the four cortex thicknesses and five strengths of trabecular bone. RESULTS: ITV, ISQ, and PTV differed significantly (p < .05) and were strongly correlated with the thickness of cortical bone (R(2) > 0.9) and the elastic modulus of trabecular bone (R(2) = 0.74-0.99). CONCLUSIONS: The initial stability at the time of implant placement is influenced by both the cortical bone thickness and the strength of trabecular bone; however, these factors are mostly nonlinearly correlated with ITV, PTV, and ISQ. Using ITV and PTV seems more suitable for identifying the primary implant stability in osteoporotic bone with a thin cortex.
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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 | 0.002 |
| 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".