Wideband Acoustic Immittance Normative Data
Bibliographic record
Abstract
This article describes the effect of ethnicity, gender, aging, and instrumentation on wideband acoustic immittance (WAI). This is an important topic to investigate as the goal of any audiological test is optimize the test's sensitivity and specificity. One way to improve the test's sensitivity and specificity is to reduce the variability of the normative data. The impact of the aforementioned demographic characteristics on WAI norms has been reviewed, and where applicable its potential impact on clinical outcome has been discussed. Overall, differences observed between Caucasian and Chinese ethnic groups in adults population may warrant the use of ethnicity-specific norms especially for detection of otosclerosis; however, these differences in the school-aged children are not large enough to warrant the use of ethnicity-specific norms. It is important to explore whether the observed differences between Caucasian and Chinese ethnic groups is due to body-size indices and whether these differences can be replicated in other East Asian ethnic groups that share similar body-size indices. The differences observed between school-aged children and adults could also potentially impact clinical decision analysis. Therefore, use of age-specific norm is recommended. The differences in WAI between different systems are not clinically significant, and the use of instrument-specific norms does not result in improved test performance at least for the detection of otosclerosis. However, measuring WAI at ambient pressure (static) or at pressure corresponding to the peak (dynamic mode) could potentially impact the normative data and may prove to be clinically useful in cases of negative and positive middle ear pressure.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".