Speech, Timbre, and Pitch Perception in Cochlear Implant Users With Flat-Panel CT-Based Frequency Reallocations: A Longitudinal Prospective Study
Bibliographic record
Abstract
HYPOTHESIS: To determine whether chronic use of experimental computed tomography (CT)-based frequency allocations would improve cochlear implant (CI) user performance in the areas of speech and music perception, as compared to the clinical default frequency mapping provided by the CI manufacturer. BACKGROUND: CIs utilize default frequency maps to distribute the frequency range important for speech perception across their electrode array. Clinical default frequency maps do not address the significant frequency-place mismatch that is inherent after cochlear implantation, nor the variability between individual anatomy or array lengths. Recent research has utilized postoperative high-resolution flat-panel CT imaging to measure the precise location of electrode contacts within an individual's cochlea, in order to generate a custom frequency map and decrease the frequency-place mismatch. METHODS: A cohort of 10 experienced CI users (14 CI ears) was recruited to receive CT scans and then use an experimental CT-based frequency map for 1 month. The efficacy of these maps was measured using a battery of speech and music tests. RESULTS: No change in speech or music performance between the Experimental and Clinical Maps was found at the group level, although there was large variability within the cohort. Greater benefit from the Experimental Map on speech in quiet tasks was correlated with better electrode array alignment in the apical (low frequency) region (rho 14 = -0.55 to -0.72, p < 0.05). CONCLUSION: This application of strict CT-based mapping was most beneficial for CI users with the least amount of apical-mid array frequency-place mismatch, and least beneficial for CI users with overly deep or shallow insertions. Results may be limited by long acclimation periods to clinical default frequency maps prior to CT map usage, intervention bias, and small sample size.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".