A Listening Effort Based Comparative Analysis of CROS Hearing Aids and Bone-Anchored Hearing Devices for Single-Sided Deafness Patients
Bibliographic record
Abstract
Single-sided deafness (SSD), characterized by the loss of hearing in one ear while the other ear retains normal hearing, poses significant challenges such as difficulties in speech-in-noise recognition, compromised sound localization, and reduced awareness of sounds in the affected auditory hemifield. Current therapeutic approaches aim to enhance sound processing from the impaired hemifield by redirecting signals to the non-impaired ear. This can be achieved through contralateral-routing-of-signal (CROS) hearing aids utilizing air conduction or bone-anchored (BA) hearing devices utilizing bone conduction. Although individuals with SSD have reported subjective benefits from both BA and CROS devices, objectively measuring and documenting these benefits has proven to be challenging. As a result, the optimal choice between these devices remains uncertain, leading to an ongoing dilemma in the clinical management of SSD. The lack of objective assessments regarding reported reductions in listening effort, as well as differences in funding modalities for each device, contribute to a longstanding controversy. This research project aims to address this controversy by investigating which device yields superior hearing outcomes for SSD patients. Subjective (NASA Task Load Index) and objective (pupillometry) measures were used to evaluate the cognitive effort required by SSD patients during speech-in-noise recognition tasks. The comprehensive results presented in this study expand upon preliminary findings previously reported at the AWC 2022 conference. These findings have the potential to provide the first comprehensive evidence guiding the management of SSD, maximizing patients' benefits, and offering evidence-based justification for funding policies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".