Cochlear Implantation in Patients With Bilateral Severe Sensorineural Hearing Loss After Major Blunt Head Trauma
Bibliographic record
Abstract
OBJECTIVE: To highlight issues that impact on cochlear implant candidacy and performance in individuals who have severe bilateral sensorineural hearing loss after major blunt head trauma. STUDY DESIGN: Retrospective case review. SETTING: Tertiary level neurotology center. PATIENTS: Twenty-five patients treated at our institution for severe hearing loss after blunt head trauma INTERVENTION: Assessment of cochlear implant candidacy criteria and postimplant outcomes in patients presenting with severe hearing loss after major blunt head trauma MAIN OUTCOME MEASURES: Cause of injury, pure tone audiology, open set speech perception (preimplant and postimplant), promontory stimulation, and imaging results. RESULTS: Of the 25 patients with severe hearing loss, 11 ultimately underwent cochlear implantation. Fall from a height was the most common cause of injury. Five patients experienced bilateral temporal bone fractures. Seventeen patients satisfied candidacy criteria for implantation. For those patients who did undergo cochlear implantation, the mean postimplant open set speech recognition score was 71. Issues related to cochlear implant candidacy and outcomes for implantation will be discussed. CONCLUSION: Severe hearing loss after major blunt head trauma presents a number of unique clinical challenges. A significant number of these patients are not suitable for cochlear implantation for a variety of reasons. Clinicians working in the area of cochlear implantation should be aware of the various problems that can impact on effective cochlear implantation in such patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".