Acoustic Basis of Tuning Fork Tests
Bibliographic record
Abstract
OBJECTIVES: To obtain pilot data to determine the feasibility of supporting or refuting a theory regarding lateralization of sound in conductive hearing loss and to describe the application of this theory to other tuning fork tests. DESIGN: Controlled audiometric testing with and without a conductive hearing loss. SETTING: A tertiary medical centre. METHODS: Sound pressure levels in external auditory canals were measured during presentation of 40- and 50-dB bone-conducted stimuli. Measurements were taken from five normal male subjects before and after inducing a conductive hearing loss. OUTCOME MEASURES: If sound intensity in the ear canal was greater in the unobstructed ear canal than the obstructed ear canal, the hypothesis was supported. The number of subjects required to provide definitive proof was calculated from the measured intensity difference with and without the conductive loss and the intrasubject variability of sound intensity measurements. The applicability of the theory to other tuning fork tests and auditory phenomena was explored. RESULTS AND CONCLUSIONS: A total of 600 subjects would be required to provide evidence to support our theory using this method. The acoustic impedance mismatch-reflected sound theory could be applied to other tuning fork tests, but until further proof is available, it must be considered only a theory.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".