Influence of Defect Depth on Resonance Frequency Analysis and Insertion Torque Values for Implants Placed in Fresh Extraction Sockets: A Human Cadaver Study
Bibliographic record
Abstract
BACKGROUND: Clinical studies show promising outcomes with implants inserted at the time of extraction. However, this often results in an initial bone defect at the marginal region which preferably should heal for an optimal function. Therefore, monitoring of these implants is vital. PURPOSES: The aims of this study were to determine the initial stability of implants placed into fresh extraction sockets, and to explore the correlations between the peri-implant bone levels and implant stability parameters. MATERIALS AND METHODS: Six human cadaver mandibles including all natural teeth were selected for this study. All natural teeth were gently extracted, and 84 implants were immediately placed into fresh extraction sockets with five different implant depths. The maximum insertion torque values were recorded, and primary implant stability measurements were performed by means of resonance frequency analysis (RFA). The vertical distance between implant/abutment junction and the first bone-implant contact was recorded using a periodontal probe. RESULTS: It was found that the insertion torque and RFA were 28.9 +/- 7 Ncm and 65.6 +/- 9 implant stability quotient (ISQ), respectively, for 420 measurements from all 84 implants. Statistically significant correlation was found between insertion torque and ISQ values (r = 0.86; p < .001) for all implants. Both insertion torque and ISQ values dramatically decreased when the amount of peri-implant vertical bone defect increased. CONCLUSION: The results of this study demonstrated a linear relationship between peri-implant vertical bone defect depth and RFA value. It is proposed that the RFA method is sensitive to detect changes of the marginal bone level and may be used to monitor healing of peri-implant bone defects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".