Combined Tuning Fork Tests in Hearing Loss: Explorative Clinical Study of the Patterns
Bibliographic record
Abstract
OBJECTIVE: To combine the tuning fork tests of Rinne, Weber, and absolute bone conduction, explore their clinical patterns, and study their combined specificity, sensitivity, and validity in assessing the hearing status of an individual. DESIGN: A cross-sectional study of the tests of Rinne, Weber, and absolute bone conduction was done on 317 adult patients from the otolaryngology outpatient clinic selected at random. All possible patterns in the combination of three tests were explored, and their interpretations worked out. The findings were compared with those of pure-tone audiometry to check their sensitivity, specificity, and validity. RESULTS AND OBSERVATIONS: Fifty-eight patterns were obtained, which were classified into 10 types (5 tables) according to the type of hearing loss. The type of hearing loss was determined in all cases, and the better ear was identified in many cases. The overall sensitivity was 76.86%, the specificity was 85.48%, and the validity was 78.54%. CONCLUSIONS: The tuning fork tests of Rinne, Weber, and absolute bone conduction, when combined and interpreted, can be reliable initial diagnostic tools. They can be used to decide whether referral to a specialist or further audiometric testing is required. The patterns presented here depict the vast number of logical possibilities wherein the authenticity of these tests can be checked and the source of errors identified.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".