Feasibility and Outcomes of an Active Osseointegrated Bone Conduction Implant in Children as Young as 5 Years of Age
Bibliographic record
Abstract
INTRODUCTION: This study reviews the feasibility of implanting active osseointegrated bone conduction devices in young children, below the prior age for FDA indication (<12 years), which has recently been reduced to 5 years. Outcomes included differences in adverse event rates and operative time between two groups (<12 and 12 years or older). MATERIALS AND METHODS: This study is a retrospective review of children receiving active osseointegrated bone conduction devices at a tertiary referral center academic hospital. One hundred and twenty-four children received 135 active osseointegrated bone conduction devices (May 2018-March 2024). RESULTS: Of 135 devices, 77 (57%) were in children <12 years (mean age (SD) = 7.9 (2.0) years, range = 4.9-11.9 years) and 58 (43%) were in 12 years or older (mean age (SD) = 15.1 (1.7) years, range = 12-18 years). Adverse events were significantly higher in the older group, occurring in 8 (10%) of 77 devices in children <12 years and 15 (26%) of 58 devices in children 12 years and older (26%) (Fisher's exact test = 0.0217 at p < 0.05). Major adverse events occurred in 5/124 (4%) patients, with 2 in patients <12 years (2/73, 3%) and 3 in children 12 and older (3/51, 6%). The proportion of major events between groups was not significantly different (Fisher's exact test = 0.4, p < 0.05). Mean surgical time was significantly less (t = -2.8799, df = 120.26, p = 0.005) in the children <12 years (mean (SD) = 66.5 (22.4) min) compared to those 12 and over (mean (SD) = 78.32 (23.1) min). CONCLUSIONS: Implantation of active osseointegrated bone conduction devices is feasible in children as young as 5 years and demonstrates low rates of complication. Further miniaturization may allow even earlier safe intervention.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".