Comparing Outcomes of Solo Neurosurgical Versus Multidisciplinary Approaches in Retrosigmoid Resection of Vestibular Schwannomas
Bibliographic record
Abstract
Introduction: Vestibular schwannomas (VS) are surgically managed using retrosigmoid, translabyrinthine, and middle cranial fossa approaches. Multidisciplinary approaches combining neurosurgery and neuro-otology are critical to improve surgical outcomes. However, in some circumstances, a combined surgical team approach is not possible, and empirical evidence directly comparing these multidisciplinary approaches to solo surgical interventions is limited. Objective: This study compares surgical outcomes in the resection of VS with the retrosigmoid approach with and without the involvement of a subspecialized neuro-otologist. Methods: A retrospective cohort study was conducted at a single quaternary hospital, assessing patients who underwent retrosigmoid VS resections between 2001 and 2023. The outcomes of interest were postoperative House-Brackman facial nerve scores, hearing preservation, operative time, blood loss, the extent of resection, and length of hospital stay. Results: A total of 65 out of a much larger cohort of patients who underwent retrosigmoid VS resection have been assessed so far; of 65 cases, 50 were operated on by both a neurosurgeon and a neuro-otologist, and 15 by neurosurgeons alone. Preliminary analysis revealed no statistically significant differences in hearing preservation, House-Brackman scores, operative time, or blood loss between the groups. The incidence of residual tumors was lower in the multidisciplinary group (34.78%) compared with the solo group (61.54%), although this difference was not statistically significant ( p = 0.16). The mean length of hospital stay was shorter for the multidisciplinary group (3.82 days) compared with the solo group (5.27 days), but this difference also did not reach statistical significance ( p = 0.106). Conclusion: We observe a trend toward greater rate of resection and a shorter hospital stay with a multidisciplinary team consisting of a neurosurgeon and a neuro-otologist compared with neurosurgeons operating alone. Publication History Article published online: 07 February 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".