62 Improvement of hearing with bevacizumab in a patient with neurofibromatosis type 2 and bilateral acoustic schwannomas
Bibliographic record
Abstract
BACKGROUND: Neurofibromatosis type 2 (NF2) is a rare genetic condition caused by mutations in the Merlin gene on chromosome 22. It results in acoustic neuromas (schwannomas) and other CNS tumors including meningiomas and ependymomas. Most patients develop hearing loss as a result of neuroma-driven destruction of auditory nerves. Surgery and radiation therapy remain the two most commonly recommended treatment options. However, there is a risk of further hearing loss with these procedures. There is emerging evidence that bevacizumab, a monoclonal antibody against VEGF-A, can shrink acoustic neuromas and mitigate hearing loss. CASE PRESENTATION: A 34-year-old female with bilateral acoustic neuromas from NF2 suffers partial hearing loss in the left ear and total hearing loss in the right ear after removal of the right-sided neuroma. Baseline MRI showed a left-sided acoustic neuroma (15 x 13 mm) and recurrence of the right-sided neuroma (18 x 14 mm). Bevacizumab was initiated at 5 mg/kg IV every 14 days. After 8 cycles, the patient reported marked improvement in hearing. At lower frequencies (< 1,000 Hz, the range of human voice), auditory thresholds improved by up to 60% of baseline, while at higher frequencies, improvements of up to 46% were seen. Repeat imaging showed no disease progression. CONCLUSIONS: Bevacizumab led to hearing improvement and prevention of disease progression after 8 cycles of therapy. This treatment should be considered in patients with NF2 and acoustic neuromas who wish to pursue a less-invasive treatment option with the potential of delaying progression and mitigating hearing loss.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".