Effect of Constraint-Induced Music Therapy in Idiopathic Sudden Sensorineural Hearing Loss
Bibliographic record
Abstract
Background: Idiopathic sudden sensorineural hearing loss (ISSNHL) is commonly encountered in audiologic and otolaryngologic practice. Constraint-induced music/sound therapy (CIMT) is characterized by the plugging of the normal ear (constraint) and the simultaneous, stimulation of the affected ear with music, which is based on a well-established neurorehabilitation approach. Corticosteroid therapy (CST) is the current mainstay of treatment. The prognosis for hearing recovery depends on many factors including the severity of hearing loss, age, and presence of vertigo. Objective: To analyze the effectiveness of CIMT with CST in ISSNHL. Methods: We performed a systematic search, using specific keywords relevant to our study, in PubMed, Cochrane Central Register of Controlled Trials, and additional sources of published trials till December 2020. We then screened all search results obtained according to our inclusion/exclusion criteria and performed a quality assessment on all studies using the Newcastle-Ottawa scale and using MedCalc, a meta-analysis was performed on suitable studies. Results: The recovery rates of three included nonrandomized studies were assessed at 1 to 3 months. A total of 229 (CST: 131, CST + CIMT: 98) patients were pooled for meta-analysis. The meta-analysis using the random-effect model found the relative risk of recovery rate within 3 months to be 1.213 (95% confidence interval 0.709-2.074), a result that is not statistically significant. Conclusion: Although our analysis results do not demonstrate the noticeable effect of CIMT in ISSNHL, it can support be a gainful adjunct to CST for better hearing results than CST alone. Therefore, it needs further prospective randomized controlled multicenter trials with a large sample.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.017 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".