Brief cognitive-behavioral training for tinnitus relief using a mobile application: A pilot open trial
Bibliographic record
Abstract
BACKGROUND: Tinnitus may be a disabling, distressing disorder whereby patients report of sounds, in the absence of external stimulus. Recent evidence supports the effectiveness of psychological interventions, particularly, cognitive behavioral therapy (CBT) based intervention for the reduction of tinnitus-related distress and disability. This study assessed the effectiveness of mobile delivered cognitive training exercises to reduce tinnitus-related distress. MATERIALS AND METHODS: Out of 26 patients diagnosed with tinnitus, 14 participants completed all 48 levels of the app. Levels of pre-post intervention tinnitus intrusiveness and handicap were evaluated using the short Hebrew version of the Tinnitus Handicap Inventory (H-THI). Mood was assessed using a Visual Analogue Scale (VAS). Participants were instructed to complete 3-4 min of daily training for 14 days. RESULTS: Repeated-measures ANOVA of completers showed a significant large-effect size reduction on H-THI scores. 50% of completers have shown reliable change (indicated by their Reliable Change Index [RCI] scores). No significant change was found in mood. DISCUSSION: Several minutes a day of training using a CBT-based app targeting maladaptive believes may decreased patients' tinnitus intrusiveness and handicap. CONCLUSIONS: Mobile apps can provide access to CBT-based interventions, using an efficient, inviting and simple platform, addressing the ramifications of tinnitus symptoms.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".