Unilateral Acoustic Neuromas: Long-Term Hearing Results in Patients Managed with Fractionated Stereotactic Radiotherapy, Hearing Preservation Surgery, and Expectantly
Bibliographic record
Abstract
INTRODUCTION: Hearing preservation is invariably a consideration when exploring treatment options for acoustic neuromas. We reviewed the long-term hearing results of patients who were treated using 1) hyperfractionated stereotactic radiotherapy (HSR), 2) hearing preservation tumor excision surgery (HPTES), and 3) expectantly (no treatment). METHODS AND MATERIALS: Single institution retrospective chart review of 42 patients managed with HSR (1993-2003), 113 patients in whom HPTES was carried out, and 86 patients who were untreated (1974-2003). Hearing levels were graded according to the Gardner-Robertson classification. RESULTS: The percent of patients managed with HSR initially who had serviceable hearing (class 1-2) was 68.8%. This fell to 6.7% in the follow-up interval. Of the group treated with HPTES, 100% had preoperative serviceable hearing. This dropped to 15.9% in the follow-up interval. The percent of patients managed expectantly who initially had serviceable hearing was 77.3%. This dropped to 33.3% during the follow-up interval. Mean follow-up periods were 4.0, 9.5, and 6.8 years in the HSR, HPTES, and expectant groups, respectively. CONCLUSIONS: Hearing acuity statistically worsened over the long term (P < .01) in all three groups. There was a significant proportion of patients in whom hearing deteriorated from serviceable to nonserviceable hearing (P < .01) during the follow-up interval. The decline was most significant in the groups treated with HPTES and HSR compared with the group treated expectantly (P < .05). Hearing outcomes, in our experience, continue to be poor, but this is especially so in patients treated with HPTES or HSR.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".