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Abstract Importance Voice-based health technologies are growing rapidly, but they lack standardized terminology, which hinders interdisciplinary collaboration, research quality, and clinical translation. Objective The objective of this work is to develop universally accepted definitions in the rapidly evolving field of vocal biomarkers, as part of the VOCAL ( V ocal Biomarker Guidelines for O ntology, C lassification, A pplication, and L ogistics) initiative, a structured, international consensus-based framework that aims to provide standards, and guidelines. Design VOCAL is a rigorous, international, multi-stage consensus-building study conducted in 2024-2025. Setting Multi-institutional collaboration between representatives from the Bridge2AI-Voice Consortium (North America) and the eVoiceNet Network (European Union), culminating in an in-person workshop at the 2025 Bridge2AI Voice Symposium. Participants A group of 24 international experts in medicine, clinical research, speech and language, audio signal processing, statistics, methodology, regulation, ethics. Methods VOCAL’s iterative process involved five rounds of review, feedback, and an in-person workshop at the international 2025 Bridge2AI Voice Symposium, ensuring the incorporation of diverse perspectives and achieving a robust agreement on the proposed definitions. Main outcomes and measures Consensus-based definitions for vocal biomarkers, spanning from broad concepts (biomarker, digital biomarker, vocal biomarker) to domain-specific measures (cardio-respiratory acoustic, voice, speech/articulatory, cognitive/language). Results A hierarchical continuum model of vocal biomarkers was established. We first distinguished between the concepts of vocal measures and vocal biomarkers. We then defined terms from broad, overarching concepts (Level 0: Biomarker, Digital Biomarker, Vocal Biomarker) to more specific physiological and cognitive domains (Level 1: Cardio-Respiratory Acoustic; Level 2: Voice; Level 3: Speech/Articulatory; Level 4: Cognitive/Language, including linguistic and paralinguistic subtypes). Conclusions and Relevance This work provides a shared vocabulary that is essential for fostering communication through interdisciplinary collaboration, improving the quality and efficiency of research and development, and ensuring the ethical, reliable, and scalable deployment of future voice-based health technologies. It lays foundational groundwork for upcoming guidelines and standards, which are crucial for advancing the field of vocal biomarkers into widespread clinical utility. Key points Question Can we establish an international consensus on the definitions related to vocal biomarkers? Findings Through a multi-stage international consensus process involving expert representatives from the Bridge2AI-Voice Consortium and eVoiceNet Network, a hierarchical model of vocal biomarkers was developed, defining terms from broad concepts (biomarker, digital biomarker, vocal biomarker) to specific physiological and cognitive domains (cardio-respiratory acoustic, voice, speech/articulatory, cognitive/language). Meaning Standardized definitions for vocal biomarkers provide an essential shared vocabulary for interdisciplinary collaboration and lay the foundational groundwork for future guidelines needed to advance the implementation of voice-based health technologies into clinical practice and clinical research.

e20001 Background: High dose melphalan followed by autologous stem cell transplant (HDM-ASCT) remains the standard of care for transplant-eligible patients with newly diagnosed multiple myeloma (NDMM) despite advances in treatments. Achievement of complete response (CR) is associated with improved overall survival (OS) and progression-free survival (PFS); Minimal Residual Disease (MRD) negativity (-) using next generation flow cytometry with a sensitivity of 10-5 is strongly associated with improved PFS and OS. We investigated the rates of conversion to MRD(-), especially MRD(-) CR following HDM-ASCT in patients with NDMM. Methods: We retrospectively reviewed patients with NDMM who underwent early HDM-ASCT at Mayo Clinic Rochester from May 2018 to July 2019. Response assessment was conducted after induction and within 100 days post-ASCT based on 2016 IMWG criteria, except for using Mass-Fix instead of immunofixation. MRD was assessed in bone marrow using the established Euro Flow protocol with sensitivity of 10-5. The FISH assay was used to risk-stratify patients as high-risk (HR) and standard risk (SR) cytogenetics as per the mSMART guidelines. Primary outcome measure was the conversion to MRD(-) CR after ASCT. Secondary outcomes include the overall conversion to MRD(-) irrespective of IMWG response, the impact of HR cytogenetics and pre-ASCT IMWG response on conversion to MRD(-) CR. Results: Two-hundred and ten patients were included; 126 (60%) were male and median age 62 years (range 32–77 years). There were 78 (44%) patients with HR cytogenetics. Pre-ASCT, 23 patients (11%) achieved MRD(-) CR, and 66 (31%) patients achieved MRD(-) CR post ASCT. Of 187 patients not in MRD(-) CR pre-ASCT, 45 (24%) converted to MRD(-) CR. Table provides a breakdown of the conversion rate of pre-ASCT IMWG response category to MRD(-) CR post ASCT and patients with MRD(+) CR had the highest rate (78%) of conversion. The presence of HR cytogenetics did not impact rates of MRD(-) CR achievement post ASCT irrespective of pre-ASCT IMWG response (p = 1.0). Overall, irrespective of IMWG response, 43 (20%) patients were MRD(-) pre-ASCT (19 in VGPR, 24 in CR or better) and 102 (49%) patients were MRD(-) post-ASCT (36 in VGPR, 66 in CR or better). Among 85 patients in VGPR post-ASCT, 36 were MRD(-) of which 8 (22%) progressed, while 49 were MRD(+) of which 24 (49%) progressed (p = 0.014). Conclusions: Upfront HDM-ASCT in patients with NDMM leads to deeper responses with 24% converting to MRD(-) CR and more than doubling of the total rate of MRD(-) irrespective of IMWG response. [Table: see text]