Speech-in-Noise Audiometry in Adults: A Review of the Available Tests for French Speakers
Bibliographic record
Abstract
BACKGROUND: Difficulty understanding speech in background noise is the reason of consultation for most people who seek help for their hearing. With the increased use of speech-in-noise (SpIN) testing, audiologists and otologists are expected to evidence disabilities in a greater number of patients with sensorineural hearing loss. The purpose of this study is to list validated available SpIN tests for the French-speaking population. SUMMARY: A review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed and Scopus databases were searched. Search strategies used a combination of 4 keywords: speech, audiometry, noise, and French. There were 10 validated SpIN tests dedicated to the Francophone adult population at the time of the review. Some tests use digits triplets as speech stimuli and were originally designed for hearing screening. The others were given a broader range of indications covering diagnostic or research purposes, determination of functional capacities and fitness for duty, as well as assessment of hearing amplification benefit. KEY MESSAGES: As there is a SpIN test for almost any type of clinical or rehabilitation needs, both the accuracy and duration should be considered for choosing one or the other. In an effort to meet the needs of a rapidly aging population, fast adaptive procedures can be favored to screen large groups in order to limit the risk of ignoring the early signs of forthcoming presbycusis and to provide appropriate audiological counseling.
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How this classification was reachedexpand
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".