Prediction of Conductive Hearing Loss Using Wideband Acoustic Immittance
Bibliographic record
Abstract
The purpose of this article was to review the effectiveness of wideband acoustic immittance (WAI) and tympanometry in detecting conductive hearing loss (CHL). Eight studies were included that measured CHL through air-and bone-conducted thresholds in at least a portion of their participants. One study included infants, three studies included children, one study included older children and adults, and three studies included adults. WAI identified CHL well in all populations. In infants and children, WAI in several single-frequency bands identified CHL with equal accuracy to measures of middle ear admittance using clinical tympanometry with a single probe tone (1000 Hz for infants; 226 Hz for children and adults). When WAI was combined across frequency bands, it identified CHL superior to traditional, single-frequency tympanometry. Only two studies used WAI tympanometry, which assesses the outer/middle ear across both frequency and introduced air pressure, and differing results were reported as to whether introducing pressure into the ear canal provides better identification of CHL. In general, WAI appears to be a promising clinical tool, and further investigation is warranted.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".