The remaining obstacles for a totally implantable cochlear implant
Bibliographic record
Abstract
PURPOSE OF THE REVIEW: For years, the development of a totally implantable cochlear implant (TICI) has faced several technical challenges hindering any prototypes from reaching full commercialization. This article aims to review the necessary specifications for a viable TICI. An overview of the remaining challenges when designing TICIs will be provided, focusing on energy supply and implantable microphones. RECENT FINDINGS: The literature review highlights how research efforts to generate sufficient power to supply a fully implantable CI could take advantage of microelectromechanical systems (MEMS)-based energy harvesters incorporating piezoelectric materials. Using one of the various energy sources in the vicinity of the temporal bone would allow the development of a self-sufficient implant, overcoming the limitations of electrochemical batteries. Middle ear implantable microphones could also use similar fabrication techniques and transduction mechanisms to meet the sensor requirements for a TICI. SUMMARY: Recent breakthroughs in power supply using MEMS-based energy harvesting technologies and piezoelectric implantable microphones may make TICIs become a more practical reality in the foreseeable future. Once available, TICIs will have major impact on our patients' quality of life and may help to make hearing rehabilitation a more appealing option to a greater proportion of those who fulfill our candidacy criteria.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".