Influence of the Frequency‐to‐Place Function on Recognition with Place‐Based Cochlear Implant Maps
Bibliographic record
Abstract
OBJECTIVE: Comparison of acute speech recognition for cochlear implant (CI) alone and electric-acoustic stimulation (EAS) users listening with default maps or place-based maps using either a spiral ganglion (SG) or a new Synchrotron Radiation-Artificial Intelligence (SR-AI) frequency-to-place function. METHODS: Thirteen adult CI-alone or EAS users completed a task of speech recognition at initial device activation with maps that differed in the electric filter frequency assignments. The three map conditions were: (1) maps with the default filter settings (default map), (2) place-based maps with filters aligned to cochlear SG tonotopicity using the SG function (SG place-based map), and (3) place-based maps with filters aligned to cochlear Organ of Corti (OC) tonotopicity using the SR-AI function (SR-AI place-based map). Speech recognition was evaluated using a vowel recognition task. Performance was scored as the percent correct for formant 1 recognition due to the rationale that the maps would deviate the most in the estimated cochlear place frequency for low frequencies. RESULTS: On average, participants had better performance with the OC SR-AI place-based map as compared to the SG place-based map and the default map. A larger performance benefit was observed for EAS users than for CI-alone users. CONCLUSION: These pilot data suggest that EAS and CI-alone users may experience better performance with a patient-centered mapping approach that accounts for the variability in cochlear morphology (OC SR-AI frequency-to-place function) in the individualization of the electric filter frequencies (place-based mapping procedure). LEVEL OF EVIDENCE: 3 Laryngoscope, 133:3540-3547, 2023.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".