Management of Traumatic Injury and Osseointegration Failure in Children With Percutaneous Bone Conduction Implants
Bibliographic record
Abstract
OBJECTIVE: This study examines the incidence and management of traumatic loss or osseointegration failure of percutaneous bone conduction implants in children. STUDY DESIGN: Case series. SETTING: Pediatric tertiary care institution. PATIENTS: Children who underwent percutaneous osseointegrated implant placement from 1996 to 2016. INTERVENTIONS: Clinical evaluation and revision surgery after implant loss. MAIN OUTCOME MEASURES: This study compares the characteristics of children who experienced traumatic loss of implant to those who did not to calculate odds ratios (ORs) describing the risk of injury and investigate device utilization after implant failure. RESULTS: One hundred forty-seven children received percutaneous bone conduction devices; 129 were followed for at least 1 year. Trauma occurred in 19 of 129 cases (15%). Among children with traumatic injury, mean age at initial surgery was 5 years (SD = ±3.3), and 42% had a developmental delay. Among children without traumatic injury, mean age at initial surgery was 6.5 years (SD = ±4.4), and 28% had a developmental delay. Multivariate logistic regression found no significant differences in age, sex, or developmental delay associated with implant loss. In five of 19 traumatic cases (26%), the implant remained in situ due to either skull fracture or abutment loss. In the remaining 14 of 19 cases (74%), there was osseointegration failure with extrusion of the implant. Seventeen children underwent revision surgery utilizing previously placed "sleeper," or backup, osseointegrated implant, and 14 (82%) of these continued to use their device. Two patients with extruded implants did not undergo revision surgery. CONCLUSION: Traumatic injury or osseointegration failure leads to loss of percutaneous bone conduction implants in approximately 15% of children. Revision surgery is often successful.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".