Evaluation of Factors Influencing Resonance Frequency Analysis Values, at Insertion Surgery, of Implants Placed in Sinus‐Augmented and Nongrafted Sites
Bibliographic record
Abstract
BACKGROUND: The immediate loading technique requires a high primary stability. Resonance frequency analysis (RFA) has been proposed to assess this stability with a quantitative method. PURPOSE: The aim of the present study was to evaluate if a good primary stability could be achieved in sites that had undergone a sinus augmentation procedure and also to evaluate the importance of different clinical factors in the determination of resonance frequency values at implant insertion. MATERIALS AND METHODS: In 14 patients, 80 implants were inserted. Sixty-three implants were inserted in a site previously treated with a sinus augmentation procedure, while 17 implants were inserted in healed or postextraction sites. For each implant, diameter, length, bone density, insertion torque, RFA value, and percentage of implant fixed to a nongrafted bone were recorded. RESULTS: Grafted sites showed high RFA values. A statistically significant positive correlation was found between resonance frequency values and implant diameter (p=0.007), implant length (p=0.02), diameter of the last bur used (p=0.01). No statistically significant correlation between RFA values and all the other variables considered was found. CONCLUSIONS: Sites treated with sinus augmentation procedures can offer good primary stability after 6 months of healing. The length and diameter of the implants, together with the geometry of the implant used, are important to obtain high RFA values.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".