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Record W1999756912 · doi:10.2310/7070.2004.03057

Characteristics of Tinnitus: Investigation of over 1400 Patients

2004· article· en· W1999756912 on OpenAlexvenueno aff
Marina Savastano

Bibliographic record

VenueThe Journal of Otolaryngology · 2004
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsnot available
Fundersnot available
KeywordsTinnitusLoudnessMedicineAudiologyAudiometryHearing lossMasking (illustration)Homogeneous

Abstract

fetched live from OpenAlex

The data in the literature concerning tinnitus characteristics are few and contrasting. Almost all data were collected by means of questionnaires mailed to the subjects, without considering the type of tinnitus and the eventual association with other otologic symptoms. To collect, in a homogeneous way and directly from patients, personal and relevant tinnitus data, we adopted a protocol of study that allowed us to select all patients suffering from idiopathic tinnitus, to obtain a wide range of information concerning the symptoms, and to compare qualitative and quantitative tinnitus data referred by patients with those obtained through audiometry. The age at which tinnitus appears more frequently is between 40 and 50 years. No significant differences between males and females were observed. The percentage of those reporting noise exposure was low. In most cases, the duration of the tinnitus was less than 1 year and more than 5 years. Loudness matching values show a homogeneous distribution for levels between 0 and 12 dB and over 15 dB, without correspondence with the subjective judgement of tinnitus intensity. Frequencies resulting high are those between 0 and 1000 Hz and those at 8000 Hz. There is a correspondence between loudness level and masking level and between loudness level and residual inhibition. Data resulting in this study underline the importance of a global evaluation of patients suffering from tinnitus, including subjective data and tinnitus measurements.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

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

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 head0.024
GPT teacher head0.257
Teacher spread0.233 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations26
Published2004
Admission routes1
Has abstractyes

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