A Systematic Review on Factors Associated With Percutaneous Bone Anchored Hearing Implants Loss
Bibliographic record
Abstract
OBJECTIVE: To investigate factors associated with percutaneous bone anchored hearing implant (BAHI) loss. DATA SOURCES: Africa-Wide, Biosis, Cochrane, Embase, Global Health, LILACs, Medline, Pubmed, and Web of Science electronic databases. STUDY SELECTION: All studies reporting on adult and/or pediatric patients with a BAHI loss were identified. Retrieved articles were screened using predefined inclusion criteria. Eligible studies underwent critical appraisal for directness of evidence and risk of bias. Studies that successfully passed critical appraisal were included for data extraction. DATA EXTRACTION: Extracted data included study characteristics (study design, number of total implants and implant losses, follow-up), patient characteristics (sex, age, comorbidities, previous therapies), and information regarding BAHI loss (etiology of loss, timing of occurrence). DATA SYNTHESIS: From the 5,151 articles identified at the initial search, 847 remained after title and abstract screening. After full text review, 96 articles were eligible. Fifty-one articles passed quality assessment, however, due to overlapping study population, 48 articles reporting on 34 separate populations were chosen for data extraction. Three hundred one implant losses occurred out of 4,116 implants placed, resulting in an overall implant loss occurrence rate of 7.3%. Failed osseointegration was responsible for most implant losses (74.2%), followed by fixture trauma (25.7%). Most losses due to failed osseointegration occurred within 6 months of the implantation. BAHI implant loss occurred more frequently in pediatric patients (p < 0.005). CONCLUSION: The current systematic review identified factors associated with BAHI loss. These factors should be considered when assessing patients' candidacy and when investigating reasons for impeded implant stability and loss.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".