{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T16:10:46Z","timestamp":1784218246504,"version":"3.55.0"},"reference-count":36,"publisher":"S. Karger AG","issue":"4","license":[{"start":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T00:00:00Z","timestamp":1773878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/karger.com\/pages\/terms-and-conditions"},{"start":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T00:00:00Z","timestamp":1773878400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/karger.com\/pages\/terms-and-conditions"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Audiol Neurotol"],"abstract":"<jats:p>Introduction: Hearing aid (HA) outcomes vary substantially despite similar hearing loss severity. We tested whether audiometric configuration improves outcome prediction beyond traditional measures. Methods: We conducted a retrospective analysis of 22,694 adults (aged 50\u201390 years) fitted with HAs across 780 French audiology centers (2018\u20132021). Eight audiometric profiles were defined a priori based on pathophysiological classifications. Machine learning models identified predictors of speech reception threshold improvement in quiet (SRT&lt;sub&gt;Q&lt;\/sub&gt;, \u22656 dB) and noise (SRT&lt;sub&gt;N&lt;\/sub&gt;, \u22652 dB). Results: Overall, 93% of participants showed demonstrated meaningful improvement in SRT&lt;sub&gt;Q&lt;\/sub&gt; and\/or SRT&lt;sub&gt;N&lt;\/sub&gt;. Adding profile membership variable increased prediction accuracy by 6% for SRT&lt;sub&gt;N&lt;\/sub&gt; and 5% for SRT&lt;sub&gt;Q&lt;\/sub&gt;. Profile-stratified analysis revealed distinct optimization pathways: Profile A (moderate presbycusis) benefited from 4 kHz amplification and follow-up appointments; Profile C (ski slope) required premium technology and high compliance; Profile D (advanced presbycusis) responded exclusively to frequency-specific precision, resisting behavioral interventions. Mid-to-high frequencies (2\u20136 kHz) drove noise outcomes; low frequencies (250\u20131,000 Hz) and clinical support predicted quiet performance. Globally, the amplification of both low and high frequencies are beneficial for both SRT&lt;sub&gt;Q&lt;\/sub&gt; and SRT&lt;sub&gt;N&lt;\/sub&gt;, and high frequencies especially for SRT&lt;sub&gt;N&lt;\/sub&gt;. Conclusion: Audiometric profiling adds significant predictive value beyond traditional severity measures, identifying profile-specific intervention levers (amplification strategy, technology; choice, compliance, follow-up appointments) for personalized optimization. This configuration-aware approach supports precision audiology, where treatment is guided by an audiometric profile and expected outcomes.<\/jats:p>","DOI":"10.1159\/000551057","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T17:00:53Z","timestamp":1773939653000},"page":"264-281","source":"Crossref","is-referenced-by-count":0,"title":["From Hearing Patterns to Functional Outcomes: Quantifying Audiometric Profiles for Precision Hearing 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