Claude code 配置 教程

Although some work has leveraged automated analyses of online communities to gain cochlear implant (CI) patient insights, there remains a gap in comparing human versus automated analysis of the nuanced, real-world experiences patients share outside clinical settings. This study characterizes experiences within the r/Cochlearimplants Reddit community and compares human to large language model (LLM) performance in annotating posts.

The UNISCREEN study investigated the feasibility of minimally invasive capillary blood sampling combined with novel antibody tests for population-wide screening of type 1 diabetes and coeliac disease autoantibodies across all age groups, with secondary objectives to evaluate the prevalence and age-related distribution of these autoantibodies in a general Northern Italian population.

While international evidence suggests seasonal variations may influence outcomes of interventions for pediatric obesity, data for Aotearoa New Zealand are limited. We examined seasonal variations in changes in body mass index standard deviation score (BMI SDS) in young people with obesity enrolled in an intervention programme.

Many biomedical articles reference multiple datasets across different public repositories, complicating accurate metadata capture and downstream re-use. Building on our prior grounded large language model (LLM) workflows for biomedical entity annotation, we extend the approach to identify and annotate all datasets referenced by a paper, even when distributed across repositories, by combining a specialized metadata schema with a three-step, search-augmented prompting strategy.

To explore the use of large language models (LLMs) to assist in developing new agent-based disease-specific patient journey models.

Sequence variability can be extremely high, particularly in bacteria due to the rapid accumulation of mutations linked to their high replication rate and environmental selection pressure, which often favors diversifying selection. For most species, there are no automated, computationally efficient tools available for constructing a nonredundant database covering the allelic variability of target proteins.

DEEPSARC, one of the first projects running on the Health Data Hub, aimed to identify real-life treatment regimens that could improve overall survival. The project is based on matching the national database of the sarcoma reference network with the SNDS.