The Normative Data for Sensorineural Acuity Level (SAL) Test among Young Adults: Comparisons Between B71 and B81 Bone Transducers
Bibliographic record
Abstract
Introduction: The sensorineural acuity level (SAL) test is a valuable clinical assessment for hearing diagnosis, especially when the results of pure tone audiometry (PTA) are incomplete or questionable. Nevertheless, the literature on the SAL test is extremely limited and more research efforts are warranted to promote the application of this test in clinical settings. Essentially, the aim of the present study was to compare the normative data for the SAL test between two different bone vibrators (Radioear B71 and Radioear B81) among Malaysian participants. Materials and Methods: Forty-two healthy Malaysian adults (aged 19 to 27 years) participated in this repeated-measures study. The PTA and SAL test was carried out according to the recommended protocols. The SAL normative data produced by the two bone transducers were then compared at different frequencies. Paired t-test and Bayesian inference were used for data analyses. Results: The normative data for the SAL test gathered in the present study were descriptively higher than those reported among Caucasian adults. Relative to the Radioear B81 bone vibrator, the SAL normative data were significantly higher for the Radioear B71 bone transducer at all tested frequencies (p < 0.001, BF10 ≥ 48.67). Conclusions: The SAL normative data established by the two bone vibrators are statistically different. This information would be beneficial to provide clinicians with appropriate normative data when performing the SAL test on Malaysian patients.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".