Data from: Large language models for automated and audience-tailored labeling of latent classes

This study compares multiple LLMs, including ChatGPT, DeepSeek, and Llama
3, to generate meaningful, audience-adapted labels for the existing latent
classes among patients with chronic low back pain (cLBP). Phenotypes were
derived from baseline data from two cohorts within the NIH HEAL BACPAC
consortium: BACKHOME, a large nationwide e-cohort (train set: N=3,025),
and COMEBACK, a deep phenotyping cohort (test set: N=450). The analysis
included pain characteristics, psychosocial factors, lifestyle habits, and
social determinants of health. ChatGPT-4o (OpenAI), DeepSeek-R1, and Llama
3 (Meta) were applied to generate class labels for each combination of
audience (clinician, patient, and caregiver), tone (formal, empathetic,
and informal), and technicality (high, medium, and low). Latent Class
Model (LCM) identified four distinct behavioral phenotypes in patients
with cLBP: High Distress and Maladaptive Behaviors, Resilient and Adaptive
Coping, Intermediate Maladaptive Patterns, and Emotionally Regulated with
High Pain Burden. Previously validated by domain experts, these profiles
served as the basis for automated labeling using three LLMs (ChatGPT-4o,
DeepSeek-R1, and Llama 3). Using different tones and complexity levels,
each model produced class labels specific to clinicians, patients, and
caregivers. The generated class names for all LLMs closely matched
expert-defined traits like emotional regulation, resilience, and high
distress, indicating strong conceptual alignment and the capacity of LLMs
to generate precise, audience-specific labels for intricate behavioral and
psychological profiles. These results highlight the possibility of
integrating LLM-driven labeling into research and clinical practice,
helping to achieve more transparent knowledge translation, improved
decision-making, and personalized care.