Resonance Frequency Analysis Assessment of Implants Placed with a Simultaneous or a Delayed Approach in Grafted and Nongrafted Sinus Sites: A 12‐Month Clinical Study
Bibliographic record
Abstract
BACKGROUND: Implant stability is one of the key factors for a successful osseointegration. At present, several techniques are available to regenerate bone tissue, but it is not clear whether implants placed in grafted bone are as stable as implants in native bone over time. PURPOSE: The aim of the present study was to compare, by means of resonance frequency analysis (RFA), the stability of implants placed in sinus-grafted and -nongrafted sites during 12-month follow-up. METHODS: Twenty-five patients received a total of 38 implants. Nineteen implants were placed in maxillary native bone (group A) and 19 implants following maxillary sinus floor augmentation using anorganic bovine bone and autogenous bone (group B) in a 50:50 ratio. Group B was divided into groups B1 and B2 depending on the timing of implant insertion, that is, B1 simultaneously and B2 6 months after sinus lift. The implants were inserted according to a two-stage procedure. RFA values were collected at baseline, 6 and 12 months after implant placement. RESULTS: Between the tested groups, no statistically significant difference was found in RFA values of implants placed in sinus-grafted and -nongrafted sites after the surgery as well as at 6 and 12 months, while a significant difference was recorded in group B1 (p = .0297) when RFA values were compared over time. CONCLUSIONS: The results of the present study suggest that regenerated bone can offer good stability for dental implants.
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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) | 0.001 | 0.000 |
| 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 | 0.001 | 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".