Listening Effort, An Overview of App Validation and Testing by the Audiology 4 all Project
Bibliographic record
Abstract
OBJECTIVE: To describe an application's development and validation process that aims to track hearing difficulties in adverse environments (a listening effort application). DESIGN: 71 subjects were evaluated, divided into two groups: 30 subjects aged between 18 and 30, and 41 subjects aged between 40 and 65. All subjects had European Portuguese as their native language; the Montreal Cognitive Assessment (MOCA) scored above 24, and all could read and write. All subjects performed the intelligibility test in noise and the test of listening effort. The two tests were randomly applied in the free field in the audiometric cabin and the application. RESULTS: There were no statistically significant differences between the results of the two methods (p>0.05). For the group aged between 40 and 65 years old, the ROC curve showed that intelligibility inferior to 68.5% and the number of correct answers lower than 1,5 in the listening effort test are the optimal cut-off for referral to further management. Both tests showed low sensitivity and specificity regarding individuals between 18 and 30 years old, indicating that the application is inappropriate for this age group. CONCLUSIONS: The application is valid and can contribute to the screening and self-awareness of listening difficulties in middle age, with a reduction in the prevalence of dementia soon.
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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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".