Towards a Unified Testing Framework for Single-Sided Deafness Studies: A Consensus Paper
Bibliographic record
Abstract
BACKGROUND: While hearing aids for a contralateral routing of signals (CROS-HA) and bone conduction devices have been the traditional treatment for single-sided deafness (SSD) and asymmetric hearing loss (AHL), in recent years, cochlear implants (CIs) have increasingly become a viable treatment choice, particularly in countries where regulatory approval and reimbursement schemes are in place. Part of the reason for this shift is that the CI is the only device capable of restoring bilateral input to the auditory system and hence of possibly reinstating binaural hearing. Although several studies have independently shown that the CI is a safe and effective treatment for SSD and AHL, clinical outcome measures in those studies and across CI centers vary greatly. Only with a consistent use of defined and agreed-upon outcome measures across centers can high-level evidence be generated to assess the safety and efficacy of CIs and alternative treatments in recipients with SSD and AHL. METHODS: This paper presents a comparative study design and minimum outcome measures for the assessment of current treatment options in patients with SSD/AHL. The protocol was developed, discussed, and eventually agreed upon by expert panels that convened at the 2015 APSCI conference in Beijing, China, and at the CI 2016 conference in Toronto, Canada. RESULTS: A longitudinal study design comparing CROS-HA, BCD, and CI treatments is proposed. The recommended outcome measures include (1) speech in noise testing, using the same set of 3 spatial configurations to compare binaural benefits such as summation, squelch, and head shadow across devices; (2) localization testing, using stimuli that rove in both level and spectral content; (3) questionnaires to collect quality of life measures and the frequency of device use; and (4) questionnaires for assessing the impact of tinnitus before and after treatment, if applicable. CONCLUSION: A protocol for the assessment of treatment options and outcomes in recipients with SSD and AHL is presented. The proposed set of minimum outcome measures aims at harmonizing assessment methods across centers and thus at generating a growing body of high-level evidence for those treatment options.
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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 | 0.686 | 0.655 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.017 | 0.020 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.036 | 0.028 |
| Research integrity | 0.033 | 0.045 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".