Anurag Rimzhim

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.

We tested for transposition effects (TEs) in Hindi (a Modern Indo-Aryan language) using unprimed lexical decision. TEs are defined as less accurate and slower responses to transposed-nonwords (e.g., ‹psate›, formed from base-word ‹paste›) than corresponding replaced-nonwords (e.g., ‹pzute›). In Hindi’s orthography, letters map transparently to phonemes (except schwa), but the letters are arranged into “akshars,” (‹[C_n]V›) which encode open syllables. This formal characteristic makes Hindi’s orthography typologically “aksharic.” We used TEs to determine whether the orthography’s typological units, letters and akshars, are also functional units for readers. We conducted three visual word recognition experiments with adult readers whose native language was Hindi. In Experiment 1, we found TEs for consonant (‹C›) and matra (‹M›, a vowel diacritic) letters, using different stimulus sets for each type of transposition. In the next two experiments, we used the same base words to form all of the transposed and replaced items. In Experiment 2, we replicated the findings of Experiment 1 in a different stimulus set; additionally, we found TEs for transpositions between a ‹C› letter and a ‹CM› akshar. In Experiment 3, we replicated results of the first two experiments by finding TEs for both consonants and matras in another stimulus set; additionally, we found similar TEs for ‹CM› akshars. These results show that ‹C› and ‹M› letters are functional units for Hindi readers; the transposition results for ‹CM› akshars are tentative. TEs for letters show that the aksharic grouping of letters does not prevent readers from decoding the constituent letters of akshars. Hindi is read alphabetically.