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Record W4313582824 · doi:10.6000/1929-6029.2022.11.21

Are the Normative Values of Sensorineural Acuity Level (SAL) Test Affected by Head Circumferences of Subjects?

2022· article· en· W4313582824 on OpenAlexvenueno aff
Mahamad Almyzan Awang, Muhammad Afiq Asyraf Suhaimi, Rosdan Salim, Nik Adilah Nik Othman, Mohd Dasuki Sul’ain, Mohd Fadzil Nor Rashid, Mohd Normani Zakaria

Bibliographic record

VenueInternational Journal of Statistics in Medical Research · 2022
Typearticle
Languageen
FieldMedicine
TopicInfrared Thermography in Medicine
Canadian institutionsnot available
FundersUniversiti Sains Malaysia
KeywordsNormativeAudiologyMedicineTest (biology)Psychology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.111
GPT teacher head0.458
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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