Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".