Vascularized Mastoid Bone Flap Cranioplasty after Translabyrinthine Vestibular Schwannoma Surgery
Bibliographic record
Abstract
OBJECTIVE: We describe a novel technique of cranioplasty using a vascularized mastoid bone flap in patients after translabyrinthine excision of vestibular schwannomas (VS). Postoperative outcomes in terms of pinna and postauricular deformity are evaluated. STUDY DESIGN: Retrospective study. SETTING: Tertiary referral center. RESULTS: Seventeen patients underwent cranioplasty with bone flap after translabyrinthine VS excision. At minimum follow-up of 12 months, none had a cerebrospinal fluid (CSF) leak. The measured pinna projections between the operated and non-operated sides had a mean difference of 0.80 mm (+/-1.70), which is not statistically significant (p > 0.05). The mean depth of the postauricular depressions was 1.38 mm (+/-0.93). Over the same period, 10 patients underwent translabyrinthine VS surgery without cranioplasty. In this group, there was a significant difference of 4.71 mm (+/-1.53) in mean pinna underprojections on the operated sides compared with the non-operated sides. Compared with patients who have undergone cranioplasty, the retroauricular depressions were significantly deeper (p < 0.05) with a mean depth of 2.92 mm (+/-1.21). CONCLUSIONS: Vascularized mastoid cortical bone flap cranioplasty is a simple and effective technique to ameliorate the pinna and retroauricular deformities after translabyrinthine VS excision without increased surgical cost or the use of alloplastic materials.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".