PurposeThe Health in Central Denmark (HICD) cohort is a newly established cohort built on extensive questionnaire data linked with laboratory data and Danish national health and administrative registries. The aim is to establish an extensive resource for (1) gaining knowledge on patient-related topics and experiences that are not measured objectively at clinical health examinations and (2) long-term follow-up studies of inequality in diabetes and diabetes-related complications.ParticipantsA total of 1.3 million inhabitants reside in the Central Denmark Region. Using register data and a prespecified diabetes classification algorithm, we identified 45 507 persons aged 18–75 years with prevalent diabetes on 31 December 2018 and a group without diabetes of equal size matched by sex, age and municipality. A 90-item questionnaire was distributed to eligible members of this cohort on 18 November 2020 (estimated time required for completion: 15–20 min).Findings to dateWe invited 90 854 persons to take part in the survey, of whom 51 854 answered the questionnaire (57.1%). Among these respondents, 2,832 persons had type 1 diabetes (55.9%), 21,140 persons had type 2 diabetes (53.2%), while 27,892 persons were part of the matched group without diabetes (60.4%). In addition to questionnaire data, the cohort is linked to nationwide registries that provide extensive data on hospital diagnoses and procedures, medication use and socioeconomic status decades before enrolment while laboratory registries has provided repeated measures of biochemical markers, for example, lipids, albuminuria and glycated haemoglobin up to 10 years before enrolment.Future plansThe HICD will serve as an extensive resource for studies on patient-related information and inequality in type 1 diabetes and type 2 diabetes. Follow-up is planned to continue for at least 10 years and detailed follow-up questionnaires, including new topics, are planned to be distributed during this period, while registry data are planned to be updated every second year.
Background Large language models have received enormous attention recently with some studies demonstrating their potential clinical value, despite not being trained specifically for this domain. We aimed to investigate whether ChatGPT, a language model optimized for dialogue, can answer frequently asked questions about diabetes. Methods We conducted a closed e-survey among employees of a large Danish diabetes center. The study design was inspired by the Turing test and non-inferiority trials. Our survey included ten questions with two answers each. One of these was written by a human expert, while the other was generated by ChatGPT. Participants had the task to identify the ChatGPT-generated answer. Data was analyzed at the question-level using logistic regression with robust variance estimation with clustering at participant level. In secondary analyses, we investigated the effect of participant characteristics on the outcome. A 55% non-inferiority margin was pre-defined based on precision simulations and had been published as part of the study protocol before data collection began. Findings Among 311 invited individuals, 183 participated in the survey (59% response rate). 64% had heard of ChatGPT before, and 19% had tried it. Overall, participants could identify ChatGPT-generated answers 59.5% (95% CI: 57.0, 62.0) of the time. Among participant characteristics, previous ChatGPT use had the strongest association with the outcome (odds ratio: 1.52 (1.16, 2.00), p=0.003). Previous users answered 67.4% (61.7, 72.7) of the questions correctly, versus non-users 57.6% (54.9, 60.3). Interpretation Participants could distinguish between ChatGPT-generated and human-written answers somewhat better than flipping a fair coin. However, our results suggest a stronger predictive value of linguistic features rather than the actual content. Rigorously planned studies are needed to elucidate the risks and benefits of integrating such technologies in routine clinical practice.
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