Efficient Estimation of the Binaural Masking Level Difference Using a Technique Based on Manual Audiometry
Bibliographic record
Abstract
PURPOSE: The Masking Level Difference (MLD) has been used for decades to evaluate the binaural listening advantage. Although originally measured using Bekesy audiometry, the most common clinical use of the MLD is the CD-based Wilson 500-Hz technique with interleaved N0S0 and N0Sπ components. Here, we propose an alternative technique based on manual audiometry as a faster way of measuring the MLD. The article describes the advantages to this administration technique and evaluates if it is a viable alternative for the Wilson technique. METHOD: Data were retrospectively analyzed on 264 service members (SMs). All SMs completed both the Wilson and Manual MLDs. Descriptive and correlational statistics were applied to evaluate the comparisons between the two techniques and highlight the differences. Equivalence measures were also completed to compare the tests using a standardized cutoff score. Analyses were also made to compare both techniques to subjective and objective measures of hearing performance. RESULTS: Moderate to high positive correlations were determined between Wilson and Manual measures of each threshold (N0Sπ and N0S0). Although the Manual and Wilson MLD techniques produced significantly different thresholds, simple linear transformations can be used to obtain approximately equivalent scores on the two tests, and agreement was high for using these transformed scores to identify individuals with substantial MLD deficits. Both techniques had moderate test-retest reliability. The Manual MLD and components had stronger correlations to the subjective and objective hearing measures than the Wilson. CONCLUSIONS: The Manual technique is a faster method for obtaining MLD scores that is just as reliable as the CD-based Wilson test. With the significant reduction in assessment time and comparable results, the Manual MLD is a viable alternative for direct use in the clinic.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".