Effectiveness of Frequency-Lowering Hearing Aids and Electric Acoustic Stimulation Cochlear Implant for Treating People with a Severe-To-Profound High-Frequency Hearing Loss
Bibliographic record
Abstract
Objectives: The objective of this research project was to compare the effectiveness of frequency-transposition, frequency-compression hearing aids and the electric acoustic stimulation (EAS) cochlear implant to improve speech recognition in participants with a sensorineural severe-to-profound high-frequency hearing loss (HFHL).Design: Ten adults with a severe-to-profound HFHL were recruited.They were all tested with frequency-compression and frequency-transposition hearing aids following an ABAC single-subject design; four-week baselines were completed with their own hearing aids, followed by eight-week trials with each device.One participant also received an EAS implant after hearing aid trials.Follow-up time ranged from 16 to 32weeks.Speech recognition was measured each week using sentence and monosyllable lists, in quiet and in noise.The subjective benefit with each technology was assessed with standardized questionnaires.Complementary data about the EAS implant effectiveness were also extracted from our database of EAS users.Results: Frequency-lowering (FL) hearing aids improved speech recognition in five participants when compared to conventional hearing aids.Others experienced either no gain or some degradation in speech recognition when using a FL algorithm.Most participants reported better speech perception in everyday listening situations with FL hearing aids.Still, the participant who received an EAS implant obtained a greater improvement in speech recognition and reported a better benefit with this technology.Data collected from our database of EAS patients validated that the EAS participant was representative of our EAS users' population. Conclusion:The EAS implant appears as the first indication for treating people with a severe-to-profound HFHL; it is also the costliest and most invasive alternative.Thus, and considering the significant benefit some participants obtained with FL hearing aids, trials using these technologies should be considered on an individual basis prior to implantation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".