The Effect of Tumor Size on Likelihood of Hearing Preservation After Retrosigmoid Vestibular Schwannoma Resection
Bibliographic record
Abstract
OBJECTIVES: 1) Describe the effect of tumor size on the likelihood of hearing preservation after retrosigmoid approach for resection of vestibular schwannoma (VS).2) Describe the effect of preoperative hearing status on the likelihood of hearing preservation. STUDY DESIGN: Retrospective chart review. SETTING: Tertiary referral center. PATIENTS: Adult (18 years or older) patients underwent retrosigmoid VS resection and postoperative audiometry between 2008 and 2018 and had a preoperative word recognition score (WRS) of at least 50%. Patients with a history of neurofibromatosis 2, radiation, or previous resection were excluded. INTERVENTIONS: All patients underwent retrosigmoid VS resection with attempted hearing preservation. MAIN OUTCOME MEASURES: WRS of at least 50%. RESULTS: Data from 153 patients were analyzed. Mean age was 50.8 (±11.3) years and mean tumor size 14 (±6) mm. Hearing was preserved and lost in 64 (41.8%) and 89 (58.2%) patients, respectively. Hearing preservation rates were higher for intrameatal tumors than for tumors with extrameatal extension (57.6% versus 29.4%, p = 0.0005). On univariate and multivariate regression analysis, tumor size (per mm increase) was a negative predictor of hearing preservation (odds ratio [OR] 0.893, p = 0.0002 and 0.841, p = 0.0005, respectively). Preoperative American Academy of Otolaryngology-Head & Neck Surgery Hearing Class was also predictive of hearing preservation (p = 0.0044). Class A hearing (compared with class B hearing) was the strongest positive risk factor for hearing preservation (OR 3.149, p = 0.0048 and 1.236, p = 0.0005, respectively). CONCLUSION: Small tumor size and preoperative class A hearing are positive predictors of hearing preservation in patients undergoing the retrosigmoid approach for VS resection.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".