{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T22:34:07Z","timestamp":1787870047175,"version":"build-2784847793"},"reference-count":22,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T00:00:00Z","timestamp":1765929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to develop and validate a predictive model that integrates audiological and psychometric variables to individually predict responses to sound therapy in tinnitus patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study included 342 patients with chronic subjective tinnitus who received standardized sound therapy. They were randomly split into training (70%) and validation (30%) sets. Using the training set, feature selection was performed via Least absolute shrinkage and selection operator (LASSO) regression, and independent predictors were identified by multivariate logistic regression. The key variables were used to build the machine learning model, and the optimal model was determined based on the area under the receiver operating characteristic curve (AUC), calibration degree, and decision curve analysis (DCA) performance. A nomogram was created for visualization, and SHAP (SHapley Additive exPlanations) values were applied to interpret the model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 342 patients were randomized into a training set (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f239, 70%) and a validation set (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f103, 30%). Multivariate logistic regression identified tinnitus duration, Tinnitus Functional Index (TFI) score, and Generalized Anxiety Disorder-7 (GAD-7) score as independent risk factors for treatment non-response, while previous treatment history, residual inhibition duration, uncomfortable loudness level, and Tinnitus Acceptance Questionnaire (TAQ) score were independent protective factors. Machine learning model comparisons revealed that the random forest model achieved the highest predictive performance (AUC\u202f=\u202f0.870), outperforming support vector machine (0.801), K-nearest neighbors (0.812), and gradient boosting (0.807) models. The model also showed good calibration and provided a positive net benefit across a wide range of threshold probabilities on decision curve analysis. SHAP-based interpretability analysis confirmed the direction and magnitude of each feature\u2019s contribution, aligning with the multivariate regression results and enhancing the model\u2019s clinical plausibility.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In conclusion, the developed nomogram integrates audiological and psychometric variables to individually predict sound therapy outcomes in tinnitus patients. This model serves as a practical tool for optimizing patient selection and personalizing intervention strategies, which may ultimately improve clinical efficacy and resource allocation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fneur.2025.1727373","type":"journal-article","created":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T05:20:30Z","timestamp":1765948830000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["An innovative predictive model for assessing tinnitus sound therapy outcomes: integrating audiological and psychometric variables"],"prefix":"10.3389","volume":"16","author":[{"given":"Gaoqing","family":"Luo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinghua","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunmei","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiqing","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,12,17]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"2040622320956426","DOI":"10.1177\/2040622320956426","article-title":"The state of the art of sound therapy for subjective tinnitus in adults","volume":"11","author":"Wang","year":"2020","journal-title":"Ther Adv Chronic 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