BAHA Skin Complications in the Pediatric Population: Systematic Review With Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Compare the incidence of skin and surgical site complications for children undergoing percutaneous and transcutaneous bone conduction implant (pBCI and tBCI) surgery via systematic review and meta-analysis of the available data. DATA SOURCES: 1) Search of PubMed, Web of Science, and EBSCOhost databases from January 2012 to April 2017. 2) References of studies meeting initial criteria. STUDY SELECTION: Inclusion criteria were studies that involved patients less than 18 years old undergoing tBCI or pBCI surgery with a BI300 implant and reported skin complications, implant loss, and need for revision surgery. Exclusion criterion was use of a previous generation implant. DATA EXTRACTION: Implants used, number of patients, age, surgical technique, Holgers score, incidence of skin complication, implant loss, and reoperation. Bias assessment performed with the Newcastle-Ottawa Scale. DATA SYNTHESIS: Twenty-two studies (14 tBCI, 8 pBCI) met criteria. Meta-analysis was performed using a random effects model. Cochran's Q score and I inconsistency were used to assess for heterogeneity. Overall estimated skin complication rate for tBCIs was 6.3% versus 30% for pBCIs (p = 4 × 10). Implant loss was 0% for tBCIs and 5.3% for pBCIs (p = 0.004). Reoperation rate was 3.0% and 6.2% for tBCIs and pBCIs respectively (p = 0.00002). CONCLUSION: There is strong evidence to suggest that in pediatric patients, the incidence of skin complications, implant loss, and rate of reoperation are higher for pBCIs compared with tBCIs. This information should be part of any discussion about BCI surgery on a pediatric patient.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".