The Rise and Fall of Aural Acoustic Immittance Assessment Tools
Bibliographic record
Abstract
Clinical assessment of middle ear function has undergone multiple transformations and developments since the first acoustic impedance measurements were made in human ears nearly a century ago. The decades following the development of the first acoustic impedance bridge by Metz in 1946 witnessed a series of technological advancements leading to the widespread use of single-frequency admittance tympanometry in the 1960s. In the 1970s, multi-frequency and multi-component tympanometry (MFT) emerged for clinical use, allowing for a better understanding of the middle ear acoustic-mechanical response at frequencies between 200 and 2,000 Hz. MFT has not gained widespread clinical adoption despite its advantages over single-frequency tympanometry. More recent technological developments enabled assessment for frequencies greater than 2,000 Hz, leading to the advent of wideband acoustic immittance measures with capabilities for comprehensive assessment of middle ear acoustic mechanics, and a great potential for use of acoustic immittance testing in various diagnostic practices. This article reviews important historical markers in the development and operation of middle ear assessment tools and analysis methods. Technical and clinical factors underlying the emergence and adoption of different acoustic immittance tests as a standard of clinical practice are described. In addition, we discuss the likelihood for widespread adoption of wideband acoustic immittance and wideband tympanometry in future clinical practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".