In vitro comparative analysis of two resonance frequency measurement devices: Osstell implant stability coefficient and Penguin resonance frequency analysis
Bibliographic record
Abstract
Adequate implant stability is an essential requirement. The introduction of the Penguin resonance frequency analysis raises some questions regarding its reliability, reproducibility, and repeatability as well as how it compares to the older Osstell device. PURPOSE: To assess the newer Penguin implant stability coefficient (ISQ) device (vs the Osstell device). MATERIALS AND METHODS: A total of 120 implants were used, divided into four groups (A, B, C, and D) (according to design) and placed in fresh bovine bone. Consecutive measurements were made with both devices (Penguin/Osstell) with their respective transducers. Then, the ISQ values were measured with the Penguin device using the Osstell transducer, and vice-versa. RESULTS: The mean insertion torque (N/cm) values for the implants were as follows: Group A = 24.7 ± 9.4; Group B = 25.6 ± 9.7; Group C = 28.7 ± 7.9; Group D = 19.1 ± 5.5. The mean ISQ values for the entire sample were as follows: Penguin 67.7 ± 6.1 and Osstell 68.5 ± 9.6. The ISQ value measured with the Penguin device using a SmartPeg transducer was 67.0 ± 8.0, and that for the Osstell device using a MultiPeg transducer was 68.3 ± 7.5. The intraclass correlation coefficient (ICC) was calculated for the ISQ values obtained from both devices and was >0.90 for all transducers. When the ICC transducers were interchanged, the values were <0.77. CONCLUSIONS: Both ISQ devices allow for reliable and repeatable measurement of implant stability; however, the use of each device-specific transducer is recommended.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".