Correlation Between Implant Stability Quotient and Bone‐Implant Contact: A Retrospective Histological and Histomorphometrical Study of Seven Titanium Implants Retrieved from Humans
Bibliographic record
Abstract
BACKGROUND: Primary stability has a relevant role in the long-term success of dental implants. A quantitative method for the measurement of implant stability has been introduced (resonance frequency analysis [RFA]). Information about the significance of RFA measurements and about the relationship between RFA values and their association with implant osseointegration, success, or failure is important from a clinical point of view. PURPOSE: The aim of the present histological and histomorphometric study was to see if a correlation existed between the bone-implant contact (BIC) percentage of retrieved human implants and RFA values. MATERIALS AND METHODS: Seven implants inserted in the posterior mandible, with a sandblasted and acid-etched surface and retrieved after a 6-month period, were evaluated in the present study. These seven implants had been retrieved for different causes. All these implants were submerged and were retrieved with a 5-mm trephine bur and immersed in 10% buffered formalin to be processed for histology. RESULTS: A statistically significant correlation could be detected between implant stability quotient and BIC (p=.016). CONCLUSIONS: Even if the relationship between bone structure and RFA is still not fully understood, in our study, a statistically significant correlation was found between RFA and BIC values. Further studies are needed to evaluate a correlation of RFA and BIC in human implants retrieved after a range of healing periods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".