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Record W2031584437 · doi:10.1055/s-0034-1383858

Facial Nerve Outcome after Vestibular Schwannoma Resection: A Comparative Meta-Analysis of Endoscopic versus Open Retrosigmoid Approach

2014· article· en· W2031584437 on OpenAlexaff
Mohammed Aref, Michael Bennardo, Forough Farrokhyar, Kesava Reddy, Abdullah Alobaid

Bibliographic record

VenueJournal of Neurological Surgery Part B Skull Base · 2014
Typearticle
Languageen
FieldMedicine
TopicMeningioma and schwannoma management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSchwannomaMedicineVestibular systemSurgeryEndoscopyFacial nerveResectionRadiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.279
GPT teacher head0.360
Teacher spread0.081 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2014
Admission routes1
Has abstractyes

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