Cochlear implant rehabilitation for patients with vestibular schwannoma: report of two cases
Bibliographic record
Abstract
OBJECTIVE AND IMPORTANCE: The objective of this paper is to highlight two main points. The primary aim is to demonstrate that cochlear implants can function in the presence of retrocochlear pathology, even after stereotactic radiosurgery (SRS), and hence to introduce this as a management option in selected patients with retrocochlear pathology, such as Neurofibromatosis type II (NFII) patients. A secondary aim is to act as a caveat that computed tomography (CT) scanning alone may not be sufficient imaging in subjects undergoing cochlear implantation (CI). CLINICAL PRESENTATION: In this paper we report two patients who underwent cochlear implant despite the presence of a vestibular schwannoma (VS) on the same side. The first case is a 59-year-old male, diagnosed with VS after 9 months of good hearing with a cochlear implant. The second case is 26-year-old female known case of NFII, received a cochlear implant after controlling the tumor growth with a SRS. CONCLUSION: We show the consequences of missing important pre-implant pathology prior to CI in one case. In both cases, we add to the literature showing that cochlear implants can work well in the presence of VS, even in the presence of previous SRS. This adds significantly to the management options available to NFII patients, and the results seem to be better than those expected for auditory brainstem implant (ABI), and with a much simpler and safer intervention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".