Are the Normative Values of Sensorineural Acuity Level (SAL) Test Affected by Head Circumferences of Subjects?
Bibliographic record
Abstract
Introduction: Sensorineural acuity level (SAL) test is believed to be helpful in estimating bone conduction thresholds in masking dilemma cases. However, before the SAL normative data can be used in clinical settings, there is a need to study the fundamental variable related to SAL normative data such as head circumference. As such, the purpose of the current study was to compare SAL normative values between subjects with bigger and smaller head circumferences at different frequencies. Materials and Methods: In this study, 48 healthy Malaysian adult subjects (aged between 18 and 50 years) were enrolled. Pure tone audiometry (PTA) and SAL test were subsequently conducted based on the recommended protocols. The SAL normative values were then compared between subjects with bigger and smaller head circumferences. Data analysis methods included paired t-test, effect size, and Bayesian approach. Results: No significant differences were noted in the SAL results when the two groups were compared, implying that the SAL normative data were not influenced by the head circumference (p > 0.05, BF10 = 0.232-0.708). Conclusions: Based on the findings of this study it appears that the SAL test results are not affected by the head sizes of the subjects. Future SAL test studies may use the normative SAL values established in the current study as a guide.
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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 | 0.003 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".