Retrospective harmonization of neuropsychiatric questionnaires in a large multi-site research consortium using expert mappings and semantic textual similarity
Abstract
Differences between clinical questionnaires limit the pooling of symptom data across studies, particularly when participants have completed different instruments. We developed a framework that uses questionnaire item content to harmonize neuropsychiatric measures without requiring overlapping response data. Experts mapped items to shared symptom dimensions, which informed score transformation and the training of models to map new items automatically. We evaluated the framework in a multisite Parkinson’s disease sample and an independent depression cohort. Transformation improved score alignment across questionnaires, while additional analyses supported clinical relevance and cross-questionnaire convergence. Our fine-tuned embedding models performing the semantic mapping task generalize to unseen questionnaires with a mean accuracy of 85.3%. This semantic, dimensional harmonization approach supports retrospective pooling of heterogeneous questionnaire data and offers a practical and systematic path toward mega-analyses on retrospective datasets.
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BibTeX
@misc{pfarr2026retrospective,
title = {{Retrospective harmonization of neuropsychiatric questionnaires in a large multi-site research consortium using expert mappings and semantic textual similarity}},
author = {Julia-Katharina Pfarr and Eva van Heese and Joshua Curtiss and Max A. Laansma and Emile d'Angremont and Conor Owens-Walton and Auwal Abdullahi and Manon Auffret and Yun Jung Bae and Bhupender Kumar Bajaj},
howpublished = {PsyArXiv},
year = {2026},
month = oct,
doi = {10.31234/osf.io/gjd7v_v1},
url = {https://osf.io/preprints/psyarxiv/gjd7v_v1/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Retrospective harmonization of neuropsychiatric questionnaires in a large multi-site research consortium using expert mappings and semantic textual similarity
AU - Julia-Katharina Pfarr
AU - Eva van Heese
AU - Joshua Curtiss
AU - Max A. Laansma
AU - Emile d'Angremont
AU - Conor Owens-Walton
AU - Auwal Abdullahi
AU - Manon Auffret
AU - Yun Jung Bae
AU - Bhupender Kumar Bajaj
PY - 2026
DA - 2026/10/22
PB - PsyArXiv
DO - 10.31234/osf.io/gjd7v_v1
UR - https://osf.io/preprints/psyarxiv/gjd7v_v1/
AB - Differences between clinical questionnaires limit the pooling of symptom data across studies, particularly when participants have completed different instruments. We developed a framework that uses questionnaire item content to harmonize neuropsychiatric measures without requiring overlapping response data. Experts mapped items to shared symptom dimensions, which informed score transformation and the training of models to map new items automatically. We evaluated the framework in a multisite Parkinson’s disease sample and an independent depression cohort. Transformation improved score alignment across questionnaires, while additional analyses supported clinical relevance and cross-questionnaire convergence. Our fine-tuned embedding models performing the semantic mapping task generalize to unseen questionnaires with a mean accuracy of 85.3%. This semantic, dimensional harmonization approach supports retrospective pooling of heterogeneous questionnaire data and offers a practical and systematic path toward mega-analyses on retrospective datasets.
ER -