Influence of Transducer Orientation on Osstell<sup>TM</sup> Stability Measurements of Osseointegrated Implants
Bibliographic record
Abstract
BACKGROUND: Resonance frequency (RF) analysis is frequently used to monitor implant stability in patients. The influence of transducer orientation on RF of implants placed in jawbone has not been evaluated. PURPOSE: The aim of this study was to evaluate to what extent transducer orientation influences RF. The second aim was to evaluate if measurements taken with any particular orientation would best relate to marginal bone levels. MATERIALS AND METHODS: Nine patients edentulous in the upper jaw received 55 implants 3 years before this study. They underwent clinical and radiographic evaluation. Using Osstell (Integration Diagnostics AB, Göteborg, Sweden), four RF measurements were made for each implant. Measurements were obtained with the transducer cantilever placed buccally (B), distally (D), palatally (P), and mesially (M). RESULTS: All implants were clinically stable. Significant differences resulted between the measurements perpendicular to the bony crest (B, P) and the parallel ones (M, D). A tendency of negative correlation was found between marginal bone levels and implant stability quotient (ISQ) measurements; however, this correlation was not statistically significant. CONCLUSIONS: In conclusion, when measuring the RF of dental implants using the Osstell, it has to be taken into account that the transducer orientation influences the measurement. It seems therefore advisable to standardize the orientation. Moreover, although there was a tendency, any statistical significant correlation between ISQ values and marginal bone levels could not be established.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".