Two Alternative Surgical Techniques for Enhancing Primary Implant Stability in the Posterior Maxilla: A Clinical Study Including Bone Density, Insertion Torque, and Resonance Frequency Analysis Data
Bibliographic record
Abstract
BACKGROUND: The primary stability of dental implants associated with resistance to micromotion during healing is affected by surgical technique and implant design, which are important especially in the soft bone, where implant failures are more likely. PURPOSES: This study was designed to compare the parameters associated with implant insertion using two different methods of enhancing implant primary stability and to identify any relationship between these parameters at implant insertion. MATERIALS AND METHODS: A total of 60 implants were placed in the maxillary posterior regions of 22 patients. The bone densities at the implant sites were recorded using a computerized tomography machine in Hounsfield unit (HU). The maximum insertion torque data were recorded with the Osseocare (Nobel Biocare AB, Göteborg, Sweden) equipment, while resonance frequency analysis (RFA) measurements were taken using an Osstell (Integration Diagnostics AB, Göteborg, Sweden) machine at implant surgery. Comparisons including HU, Ncm, and implant stability quotient were made between two control groups (C1 and C2), and corresponding four test groups (T1-T4) using thinner drills to enhance primary implant stability. RESULTS: Two implants were lost, meaning an overall implant survival rate of 96.6% after 3 +/- 1 years. When compared to control groups, significantly higher mean maximum insertion torque and RFA values were found for corresponding test groups. In addition, strong correlations were observed between the bone density and insertion torque, and implant stability values at implant placement. CONCLUSION: The results of this study suggest that using thinner drills for implant placement in the maxillary posterior region where bone quality is poor may improve the primary implant stability, which helps clinicians to obtain higher implant survival rates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".