MétaCan
Menu
Back to cohort
Record W2915691501 · doi:10.1044/2018_aja-17-0115

Comparison of Tinnitus Loudness Measures: Matching, Rating, and Scaling

2019· article· en· W2915691501 on OpenAlex
Candice Manning, Leslie D. Grush, Emily J. Thielman, Larry E. Roberts, James A. Henry

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Journal of Audiology · 2019
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsMcMaster University
FundersU.S. Department of Veterans Affairs
KeywordsLoudnessTinnitusAudiologyPsychologyMedicine

Abstract

fetched live from OpenAlex

Purpose Chronic tinnitus ("ringing in the ears") is a phantom auditory perception with no cure. A goal of treatment is often to reduce the loudness of tinnitus. However, tinnitus loudness cannot be measured objectively. It is most commonly assessed by obtaining a loudness match (LM) with a pure tone and by using a numeric rating scale (NRS). Constrained loudness scaling (CLS) is a more recent measure of tinnitus loudness that utilizes auditory training of a fixed loudness scale to guide tinnitus loudness judgments. The purpose of this study was to compare results using these 3 measures of tinnitus loudness. Method This study obtained tinnitus loudness measures of LM, NRS, and CLS with 170 participants. These participants are part of a larger study obtaining repeated measures over 6 months. Only baseline data are presented. Results Correlations between all measures were weak to moderate: LM versus CLS ( r = .46), CLS versus NRS ( r = .49), and LM versus NRS ( r = .38). Conclusion Further systematic research is needed to more fully understand the relationships between these different measures and to establish a valid measure of tinnitus loudness.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

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

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 head0.039
GPT teacher head0.342
Teacher spread0.303 · 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