Features of complex diagnostics of subjective ear noise
Bibliographic record
Abstract
Tinnitus is one of the most common ENT complaints. The peculiarity of this disease is lack of objective measurements. It is hard to monitor evaluation of treatment results. The main reference points, such as the presence or absence of ear noise – are not sufficient to determine this pathology. Also, most of these patients have negative emotions connected with their ear noise. That is why the characteristics of tinnitus and its influence on the quality of life are very individual. Thus, it is advisable to distinguish two components of ear noise: noise itself, as a physical quantity that has amplitude and frequency parameters, and noise, as the main source of subjective suffering of the patient. To determine the characteristics of these two noise components, we used two diagnostic methods: standard noise measurement and a visually analog scale to assess the degree of discomfort caused by ear noise. In current study, we investigated interconnection between tinnitus intensity measured by tinnitus matching and data of visual-analog scale of subjective perception of tinnitus. We evaluated that severity of tinnitus intensity did not correlate with thresholds in tinnitus pitch matching. In addition, with the help of international questionnaire survey SCL-90-R we revealed correlation between subjective noise level and patient’s psycho-emotional condition.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".