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Translation and Cross-Cultural Adaptation of the Chronic Rhinosinusitis Control Test for Global Use

  • Hye K. Pae*
  • , Detong Xia
  • , Hanzhong Sun
  • , Yudi Chen
  • , Kwangoh Yi
  • , Minjeong Song
  • , Eriko Sato
  • , Ali R. Abasi
  • , Jody Ballah
  • , Anna Babarczy
  • , Raymond Bertram
  • , Agnieszka Biernacka
  • , Mable Chan
  • , Teresa Civera
  • , Irina Dubinina
  • , Doğu Erdener
  • , María Isabel Maldonado García
  • , Ali Garib
  • , Tuomo Häikiö
  • , H. O. Fuk-chuen
  • Li Yu Hung, R. Malatesha Joshi, Oksana Kanerva, Kiranpreet Kaur Baath, Björn Köhnlein, Dalibor Kučera, Paula Luegi, Yustinus Calvin Gai Mali, Sivan Medina, Stefan Milosavljević, Amna Mirza, Mohamed Y. Mwamzandi, Fatemeh Nami, Anabella Gloria Niculescu-Gorpin, Portia Padilla, Georgia Panayiotou, Manuel Perea, Luciano Perondi, Hiển Phạm, Rasmus Puggaard-Rode, Anurag Rimzhim, Sreeparna Sarkar, David L. Share, Gláucia V. Silva, Antônio R.M. Simões, Charlotte Stormbom, Titima Suthiwan, Katsuo Tamaoka, Mila Tasseva-Kurktchieva, Paweł Urbanik, An Van, Katie M. Phillips, Ahmad R. Sedaghat*
*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

Abstract

Introduction: The Chronic Rhinosinusitis Control Test (CRCT) is a patient-reported outcome measure (PROM) written in English that is psychometrically validated to measure chronic rhinosinusitis control. Because the availability of translated PROMs is a driver of data equity—collection of data that is fair and generally representative—our objective was to create a library of translated, cross-culturally adapted versions of the CRCT that could ultimately be used for patients worldwide. 

Methods: A hybrid approach leveraging generative artificial intelligence (genAI) in collaboration with expert human linguists was employed for translation and cross-cultural adaptation of the CRCT. For each target language, forward translations were performed with three large language models (LLMs) (ChatGPT, Copilot, and Perplexity) after which an expert human linguist provided additional revisions that were used to create a consensus final translation. Backward translations were performed using LLMs (Claude, Copilot, and Perplexity). The accuracy and validity of translations at each step were assessed qualitatively and quantitatively. 

Results: The translation and cross-cultural adaptation of the CRCT was achieved into 37 languages: Arabic, Bengali, Brazilian Portuguese, Bulgarian, Cantonese Chinese, Czech, Danish, Dutch, European Portuguese, Filipino, Finnish, French, German, Greek, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Mandarin Chinese, Norwegian, Persian, Polish, Punjabi, Romanian, Russian, Serbo-Croatian, Spanish, Swahili, Swedish, Thai, Turkish, Ukrainian, Urdu, and Vietnamese. These translated, cross-culturally adapted versions of the CRCT are made available in this article. 

Conclusion: Translated, cross-culturally adapted versions of the CRCT developed in this study promote data equity by serving as a basis for psychometric validation of the CRCT for worldwide use.

Original languageEnglish
Number of pages11
JournalInternational Forum of Allergy and Rhinology
DOIs
Publication statusE-pub ahead of print - 9 Jun 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

User-Defined Keywords

  • AI
  • artificial intelligence
  • ChatGPT
  • chronic rhinosinusitis
  • chronic rhinosinusitis control test
  • claude
  • copilot
  • cross-cultural adaptation
  • perplexity
  • translation

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