An advanced software tool for evaluation and rehabilitation of cochlear prosthesis users
Bibliographic record
Abstract
Cochlear implants are now widely used as a mean for restoring hearing to profoundly deaf people. There is a great variety in performance of the implanted patients, this depends on the state of the cochlea and the remaining nervous terminations, and also it depends on the site and degree of insertion of the electrodes, on the stimulation strategy and even on the rehabilitation process. After the implantation of the prosthesis, the patient must go through a fitting process, and a long procedure of evaluation and rehabilitation. This paper will present an advanced software used by physicians and technicians for the fitting of cochlear implants and the evaluation of the outcomes and that can be used by either specialists or patients during the rehabilitation process. This tool is essentially composed of two parts. The first part is used for the fitting of the implant; it determines the dynamic range of the electric current that can be used with the patient. It also sets which of the different stimulation channels is exploitable. The second part is used for the testing and the evaluation of the outcomes of different stimulation strategies. This part can be used by the clinician as well as by the patient alone to practice with recorded sounds. All published stimulation strategies can be tested with this software and it's easy to use with all the known cochlear prostheses. It is also suitable for researchers that want to experience new stimuli patterns or stimulation algorithms.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".