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Wideband Reflectance Norms for Caucasian and Chinese Young Adults

2006· article· en· W2063078775 on OpenAlexaff
Navid Shahnaz, Karin T. Bork

Bibliographic record

VenueEar and Hearing · 2006
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsReflectivityWidebandPsychologyAudiologyMedicineOpticsPhysics

Abstract

fetched live from OpenAlex

In Brief Objective: This study examined differences between the middle ears of two ethnic groups, Caucasian and Chinese young adults with normal hearing, using a new middle-ear measurement technique, wideband energy reflectance. The goal of this study was to determine whether the Chinese group had different middle-ear transmission properties than the Caucasian group. Design: There were 126 subjects (237 ears) between the ages of 18 and 32 yr, with 62 subjects in the Caucasian group and 64 subjects in the Chinese group. Wideband energy reflectance data were gathered using Mimosa Acoustics (RMS system version 4.0.4.4) wideband reflectance (WBR) equipment. Results: The Chinese group had significantly lower wideband energy reflectance than their Caucasian counterparts at higher frequencies; however, the Caucasian group had significantly lower energy reflectance at lower frequencies than the Chinese group. The Chinese group also had significantly lower admittance magnitude than the Caucasian group at lower frequencies. Because body size indices are more comparable between Caucasian females and Chinese males, the effect of body size could be potentially adjusted for by comparing the Caucasian female subjects with the Chinese male subjects. The differences observed between the Caucasian and the Chinese groups were no longer significant when the Caucasian female subjects were compared with the Chinese male subjects. Applying the Caucasian norms to four Caucasian adults with surgically confirmed otosclerosis resulted in an improved hit rate compared with the combined Caucasian and Chinese norms and the Chinese-only norms. Conclusion: Body size may play a role in the observed differences between the Caucasian and Chinese groups. The findings of this study suggest that further research is needed to investigate the effects of body size on wideband energy reflectance. It should be noted that factors other than body size may contribute to the observed differences. Chinese individuals may simply have different middle-ear characteristics than Caucasian individuals, which could affect WBR. In the meantime, overall test performance may be improved by using a more homogeneous norm when evaluating the middle ear of Caucasian or Chinese individuals with WBR. The purpose of this study was to examine whether middle-ear transmission properties differ among Caucasian and Chinese males and females, when measured with commercially available wideband reflectance (WBR) equipment. This is an important issue to investigate as overall test performance could potentially be improved using a more homogeneous norm when evaluating the middle-ear of Caucasian or Chinese individuals with WBR. 126 subjects (62 Caucasian and 64 Chinese) were tested using Mimosa Acoustics WBR equipment. It was found that the Chinese group had significantly lower WBR than their Caucasian counterparts at higher frequencies; however, the Caucasian group had significantly lower energy reflectance at lower frequencies than the Chinese group. It seems that body size may play a role in the observed differences between the Caucasian group and Chinese groups. It should be noted that factors other than body size may contribute to the observed differences. Chinese individuals may simply have different middle-ear characteristics than Caucasian individuals that could affect WBR.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.009
GPT teacher head0.263
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations97
Published2006
Admission routes1
Has abstractyes

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