Facial Nerve Outcome after Vestibular Schwannoma Resection: A Comparative Meta-Analysis of Endoscopic versus Open Retrosigmoid Approach
Bibliographic record
Abstract
The minimal access retrosigmoid endoscopic approach to vestibular schwannoma (VS) resection has been used with promising results. However, it has not been compared with the standard open approach in the literature. We performed a meta-analysis review for all articles describing both approaches for VS from 1996 to 2011. We found 1861 articles. After review and discussion, we narrowed our study to 25 articles, 4 endoscopic and 21 open. The total number of patients was 3026 for open and 790 for endoscopic. The mean tumor sizes in the open and endoscopic series were 2.5 cm and 2.7 cm, respectively. Good facial nerve outcome was achieved in 67% of the open series patients and in 94% of the endoscopic series patients. Other outcomes in the open and endoscopic series were the following: gross total resection, 91% versus 97%; functional hearing, 22.6% versus 46%; wound infection, 1.3% versus 2.6%; and recurrence, 5.4% versus 2.2%. We acknowledge the limitations of our study, but we can state that the endoscopic approach is not inferior to the standard open approach. In expert hands the endoscopic approach can offer as good a result as the open, with potential benefits such as less pain and a shorter length of stay in the hospital. There is a need for more controlled studies for a definitive comparison.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.022 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".