Wideband Acoustic Immittance Normative Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it