MétaCan
Menu
Back to cohort

Pathways

2012· article· en· W2315832285 on OpenAlexaff
Frank E. Musiek, Greg Noel

Bibliographic record

VenueThe Hearing Journal · 2012
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsDalhousie University
Fundersnot available
KeywordsHeadphonesNormativeActive listeningNoise (video)AudiologySentenceTest (biology)PsychologyReplicateComputer scienceTask (project management)Speech recognitionMedicineCommunicationArtificial intelligenceMathematicsStatisticsAcousticsEngineeringLaw

Abstract

fetched live from OpenAlex

Figure. iStockphotoDifficulty hearing in noise affects approximately 10 percent of patients seeking audiology services. (Ear Hear 1989;10[3]:200.) Researchers and audiologists have struggled to define and replicate this problem clinically and have had difficulty in determining the type of stimuli, noise, and signal-to-noise ratio (SNR) that best imitates real-life situations. Attention, cognition, and language are also a hindrance. The complexity of our central auditory system and an attempt to determine the sites responsible for such processing also provide complications. The test Listening in Spatialized Noise-Sentences (LiSN-S) recently developed by Australian researchers may have sparked renewed interest in tackling this challenging task. (J Am Acad Audiol 2008;19[5]:377.)Figure: Frank Musiek, PhDFigure: Greg Noel, MScThe LiSN-S, distributed by Phonak, is a computer-based software platform that comes with standard introductions to patients. The final scoring is done by computer while the clinician enters the number of correct words for each target sentence in noise. The test takes approximately 20 minutes to administer, and the program generates a report at the end, comparing the patients' scores with normative data. Normative data are included with the test beginning at age 6. The test has been normalized for use in North America. (J Am Acad Audiol 2010;21[10]:629.) Recent published normative data up to age 60 have also been reported. (J Am Acad Audiol 2011; 22[10]:697.) The LiSN-S uses mathematical algorithms (head-related transfer functions) to reproduce a three-dimensional space under headphones. Four subtests are designed to be administered in a standard order. Each subtest presents sentences binaurally under headphones with competing stories. The patient repeats as many words as possible from the target sentence while also listening to a story. The level of the target sentences varies in an adaptive approach to find the signal-to-noise ratio at which 50 percent of the words in the target sentences are understood and repeated by the client. Performance indicators are generated for the clinician, with two speech-reception threshold (SRT) and three advantage scores. Interestingly, while the target sentences are always presented from the front, the competing stories are spoken by the same voice or a different voice and can arrive from the front, left, or right side of the patient. This generates four conditions for the patient: same voice from same direction, different voices from same direction, same voice from different directions, and different voices from different directions. These conditions are reflective of situations that can occur in patients' everyday lives. Three different advantage scores can be calculated for the four conditions. The talker advantage (the difference between the same voice from same direction and different voices from same direction) shows the dB improvement in SRT by using information about the different voice qualities of the competition relative to the target voice. The spatial advantage (difference in dB between the same voice from same direction and different voices from different directions) shows the amount of dB that the SRT can be improved by using different spatial locations. The total advantage (difference between the same voice from same direction to different voices from different directions) reflects in dB the amount of improvement by combining the different vocal qualities and different spatial locations. Two individual subtest scores are also generated, the low-cue and high-cue SRTs, which are of great interest to the clinician. The low-cue condition (same voice with same direction) reveals the SNR required when there are no talker or spatial cues available to the client. The high-cue condition (different voices from different directions) measures the SNR required when talker and spatial cues are available. Researchers suggested that the high-cue SRT has the potential to be affected by the widest range of disorders of all the scores measured in the LiSN-S. (J Am Acad Audiol 2012;23[2]:97.) This was discovered while attempting to prioritize patients for auditory processing testing, and it may be useful to assess patients with this subtest first. High-cue SRT scores approaching near normal levels may suggest that the patient will not have problems utilizing spatial cues or talker cues. Screening this way may take approximately five minutes. Further testing with the remaining portions of the test can help delineate potential problems if the patient scores poorly on this subtest. Difficulty finding professionals who provide treatment for those diagnosed with auditory processing disorder is a common reason for audiologists not offering auditory processing testing. (J Am Acad Audiol 18[5]:428.) A rehabilitation program has been developed called LiSN & Learn to help patients with processing problems in background noise. (J Am Acad Audiol 2011;22[10]:697.) Preliminary data on deficit-specific remediation from nine participants demonstrated that, on average, children improved their SRT by 10 dB over the course of the 12-week training. Children as young as 6 years are able to complete training and the improvements last for three months posttraining. The LiSN & Learn software is available through the National Acoustic Laboratories website. (See FastLinks.) Great strides have been made in validating more efficient and effective tests to add to audiologists' repertoire. We are perhaps one step closer to understanding the complex processing involved in hearing in the presence of background noise and being able to offer solutions for our patients' hearing problems. The LiSN-S is based on cutting-edge research and it will be interesting to watch the clinical uptake of this test and its effects on assessing and, with the LiSN & Learn training software, remediating hearing-in-noise complaints. FastLinks Visit National Acoustics Laboratories' website at http://bit.ly/NatAcousLab. Read past Pathways columns in a special collection at http://bit.ly/PathwaysCollection. Visit HJ's Student Blog at http://bit.ly/HJStudentBlog. Check out HJ's R&D Blog at http://bit.ly/HJblogRD. Click and Connect! Access the links in The Hearing Journal by reading this issue on our website or in our new iPad app, both available at thehearingjournal.com. Comments about this article? Write to HJ at [email protected]. Follow us on Twitter at twitter.com/hearingjournal and like us on Facebook at www.facebook.com/HearingJournal.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.172
GPT teacher head0.430
Teacher spread0.258 · 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 designNot applicable
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
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueThe Hearing JournalSame topicNoise Effects and ManagementFrench-language works237,207