Longitudinal Evaluation of Bone-Anchored Hearing Aid Implant Stability Using the Advanced System for Implant Stability Testing (ASIST)
Bibliographic record
Abstract
OBJECTIVE: This study aims to provide a clinical evaluation of the Advanced System for Implant Stability Testing (ASIST) for assessment of implant stability for bone-anchored hearing aid patients. We evaluate the longitudinal changes in implant interface stability during the first year following surgery. METHODS: ASIST measurements were collected for 39 patients selected to receive a bone anchored hearing aid for hearing loss. Measurements were collected at the time of surgery and at 3 days, 2 weeks, 1 month, 3 months, 6 months, and 12 months following surgery. Longitudinal changes in ASIST Stability Coefficient (ASC) were determined for each patient. Correlations were investigated between initial implant stability as measured by the ASC and clinical parameters such as operating surgeon, patient age at surgery, and implant type. RESULTS: ASC values ranged from 11.9 to 137.0 (31.9 ± 18.0). On average, there was a slight decrease in ASC up to 3 months after surgery followed by an increase up to 1 year. Preliminary results presented in this study suggest that there may be differences in the initial stability between operating surgeons (p = 0.0012; p = 0.0049) and there was a trend toward possible differences between different implant types. CONCLUSION: We have shown promising results using the ASIST in a clinical setting for longitudinal evaluation of bone-implant interface integrity. Isolating the interface properties from the implant-abutment system allows for objective comparisons across patients that are not possible with other stability measurement systems.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".