Preoperative Sudden Hearing Loss May Predict Hearing Preservation After Retrosigmoid Resection of Vestibular Schwannoma
Bibliographic record
Abstract
OBJECTIVES: Describe the effect of preoperative sudden hearing loss (SHL) on likelihood of hearing preservation (HP) after surgical resection of vestibular schwannoma (VS). STUDY DESIGN: Retrospective chart review. SETTING: Tertiary referral center. PATIENTS: Adult patients (≥18 years) who underwent retrosigmoid VS resection for HP between February 2008 and December 2018 were reviewed. All patients had preoperative word recognition score (WRS) of at least 50%. Similarly, HP was defined as postoperative WRS of more than or equal to 50%. Regression analysis was used to describe the effect of SHL on HP, accounting for tumor size, and preoperative hearing quality. INTERVENTIONS: All patients underwent retrosigmoid VS resection for HP. MAIN OUTCOME MEASURES: WRS of at least 50%. RESULTS: Of 160 patients who underwent retrosigmoid VS resection during the study period, 153 met inclusion criteria. Mean tumor size was 14.0 (±6) mm. Hearing was preserved in 41.8% (n = 64). Forty patients (26.1%) had a history of preoperative SHL. Among 138 patients (90.2%) in whom the cochlear nerve was anatomically preserved during surgery, HP was achieved in 61.8% of those with SHL (21 of 34) and 41.3% of those without SHL (43 of 104) (p = 0.0480). On univariate and multivariate analysis (accounting for tumor size and preoperative hearing quality), SHL was a significant positive predictor of HP (odds ratio 2.292, p = 0.0407 and odds ratio 2.778, p = 0.0032, respectively). CONCLUSION: In patients with VS and retained serviceable hearing, SHL is an independent predictor of HP after retrosigmoid microsurgical resection when the cochlear nerve is preserved.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".