Bibliographic record
Abstract
PURPOSE OF REVIEW: Cochlear implantation (CI) is a viable option for patients with severe sensorineural hearing loss. Advances in CI have focused on minimizing cochlear trauma to improve hearing preservation outcomes, and in doing so expanding candidacy to patients with useful cochlear reserve. Robotics holds promise as a potential tool to minimize intracochlear trauma with electrode insertion, improve surgical efficiency, and reduce surgical complications. The purpose of this review is to summarize efforts and advances in the field of robotic-assisted CI. RECENT FINDINGS: Work on robotics and CI over the past few decades has explored distinct surgical aspects, including image-based surgical planning and intraoperative guidance, minimally invasive robotic-assisted approaches mainly through percutaneous keyhole direct cochlear access, robotic electrode insertion systems, robotic manipulators, and drilling feedback control through end effector sensors. Feasibility and safety have been established and many devices are undergoing clinical trials for clinical adoption, with some having already achieved approval of national licensing bodies. SUMMARY: Significant work has been done over the past two decades that has shown robotic-assisted CI to be feasible and safe. Wider clinical adoption can potentially result in improved hearing preservation and quality of life outcomes to more CI candidates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.004 | 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".