Does Tinnitus Lead to Depression?
Bibliographic record
Abstract
PURPOSE: The aim of the study was to investigate the correlation between THI (Tinnitus Handicap Inventory) and BDS (Beck Depression Scale). MATERIALS AND METHODS: High frequency thresholds and PTA (Pure Tone Audiometer) thresholds for the patients were measured in 44 patients with tinnitus (bilateral=13; unilateral=31). Tinnitus frequency and intensity were measured using one-pair method with high frequency audiometer Interacoustic AC40. Applied BDS and THI scores are evaluated for all patients. Our findings are analysed statistically with SPSS v.21 and BDS and THI correlation with tinnitus intensity and frequency was executed. RESULTS: The mean value of tinnitus frequency was 10 kHz (min 0.25 kHz, max16 kHz and SD 4.26), mean tinnitus intensity was 50.6 dB (min 15 dB, max 110 dB and SD 26.9 dB) mean THI score was 38.04 (min 10, max 86 and SD 20.03) and mean BDS score was 9.45 (min 0, max 28 and SD 6.49). There was no statistical correlation between THI score and tinnitus frequency (r=0.055, p=0.787). Moderate correlation is obtained between tinnitus frequency and depression (r=0.6, p=0.001). There were weak correlations between tinnitus intensity and THI score and (r=0.3, p=0.09) and between tinnitus intensity and BDS score (r=0.28, p=0.13). Although a statistically significant difference was observed between THI scores of patients with bilateral and unilateral tinnitus (p0.05). High frequency thresholds and UCL scores of ears with tinnitus were not statistically different from ears with no tinnitus (p>0.05). CONCLUSION: No correlation was seen between THI and tinnitus frequency, but a moderate correlation was seem between BDS score and tinnitus frequency. There were also weak correlations between tinnitus intensity and THI and BDS scores.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".