Biomechanical Aspects of Primary Implant Stability: A Human Cadaver Study
Bibliographic record
Abstract
BACKGROUND: The quality of bone is an important factor in the successful implant treatment, and it is evident that higher implant failure is more likely in poor quality of bone. The primary stability of oral implants related to resistance to micromotion during healing is influenced by bone quality, surgical technique, and implant design. PURPOSES: The aims of this biomechanical study were to explore the effect of bone quality on initial intraosseous stability of implants, and to determine the correlations between the bone quality and implant stability parameters. MATERIALS AND METHODS: Twenty-four implants (Neoss Ltd., Mölnlycke, Sweden) were placed into anterior and posterior regions of three human cadaver mandibles. The bone densities of implant recipient sites were preoperatively determined using computerized tomography (CT) in Hounsfield unit (HU). The maximum insertion torque values were recorded, and primary implant stability measurements were noninvasively performed by means of resonance frequency analysis (RFA). RESULTS: The bone density values ranged from -267 HU to 553 HU. It was found that mean bone density, insertion torque, and RFA values were 113 +/- 270 HU, 41.9 +/- 5 Ncm, and 70 +/- 7 implant stability quotient (ISQ), respectively. Statistically significant correlations were found between bone density and insertion torque values (r = 0.690, p < .001); bone density and ISQ values (r = 0.557, p < .05); and insertion torque and ISQ values (r = 0.853, p < .001). CONCLUSION: CT is a useful tool to assess bone quantity and quality in implant recipient sites, and bone density has a prevailing effect on implant stability at placement.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".