Resonance Frequency Analysis Identifies Implant- and Host-Related Factors Associated With Bone-Anchored Hearing Implant Stability
Bibliographic record
Abstract
HYPOTHESIS: Resonance frequency analysis (RFA) is a reliable, noninvasive method to assess the stability of bone-anchored hearing implants (BAHIs), although surgical-, implant-, and host-related factors can affect its outcome. BACKGROUND: BAHI plays an important role in restoring hearing function. However, implant- and host-related factors contribute to premature implant extrusion. To mitigate this, noninvasive methods to assess implant stability, along with a better understanding of factors contributing to BAHI failure, are needed. METHODS: We evaluated the utility of RFA to quantify implant stability in sawbone (bone mimicking material), 29 human cadaveric samples, and a prospective cohort of 29 pediatric and 27 adult participants, and identified factors associated with implant stability. To validate the use of RFA in BAHI, we compared RFA-derived implant stability quotient (ISQ) estimates to peak loads obtained from mechanical push-out testing. RESULTS: ISQ and peak loads were significantly correlated (Spearman rho = 0.48, p = 0.0088), and ISQ reliably predicted peak load up to 1 kN. We then showed that in cadaveric samples, abutment length, internal table bone volume, and donor age were significantly associated with implant stability. We validated findings in our prospective patient cohort and showed that minimally invasive Ponto surgery (MIPS; versus linear incision), longer implantation durations (>16 wk), older age (>25 yr), and shorter abutment lengths (≤10 mm) were associated with better implant stability. Finally, we characterized the short-term reproducibility of ISQ measurements in sawbone and patient implants. CONCLUSIONS: Together, our findings support the use of ISQ as a measure of implant stability and emphasize important considerations that impact implant stability, including surgical method, implant duration, age, and abutment lengths.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".