Effects of Varying Unilateral Conductive Hearing Losses on Speech-in-Noise Discrimination: An Experimental Study with Implications for Surgical Correction
Bibliographic record
Abstract
HYPOTHESIS: Unilateral conductive hearing loss (HL) causes measurable disability in realistic hearing environments. The benefits of improving this loss depend both on the absolute level of final hearing and on hearing asymmetry. BACKGROUND: Surgical success is often measured by the degree of change in the air-bone gap or in the air thresholds. The Glasgow Benefit Plot and the Belfast Rule of Thumb suggest that a given hearing gain will be of greater benefit if the postoperative thresholds in the worse ear are a <30-dB HL or within 15 dB of the contralateral ear. This assertion has important surgical implications, but few audiometric data supporting it currently exist. METHODS: Speech-in-noise sound-field scores were measured in 16 normal volunteers at three presentation levels at two signal-to-noise ratios (SNRs). Two levels of unilateral conductive HL were simulated with earplugs, averaging 25-and 43-dB HL, respectively, and the effect on speech-in-noise scores was measured and analyzed. RESULTS: Unilateral conductive HL is a disadvantage at lower sound intensities and low SNRs, but it can be compensated for by increasing volume or SNR. The benefits of improving unilateral conductive HL are greater if the final asymmetry is <25 dB and the final hearing threshold is <25 dB. CONCLUSIONS: Unilateral conductive HL is a significant disadvantage at low SNRs or presentation volumes. The benefits of surgery to improve HL depend not only on the degree of hearing improvement but also on the final hearing threshold in both ears.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".