Advancing the Prediction and Understanding of Placebo Responses in Chronic Back Pain Using Large Language Models.

Area of Science:

• Computational linguistics
• Clinical psychology
• Pain management

Background:

• Placebo analgesia is a complex phenomenon with variable individual responses.
• Previous research on biopsychosocial factors is limited by generalizability and ecological validity.

Purpose of the Study:

• To assess fine-tuned large language models (LLMs) for predicting placebo responses.
• To extract contextual features from patient interviews for a data-driven approach.

Main Methods:

• LLMs analyzed patient interviews from two randomized controlled trials (RCTs) for chronic low-back pain.
• Features were extracted from interviews conducted post-treatment (discovery) and pre-treatment (validation).

Main Results:

• LLMs predicted placebo response with 74% accuracy in unseen data and 70% in an independent cohort.
• LLMs enabled a fully data-driven approach, eliminating the need for pre-selected search terms.
• Interpretable insights into psychosocial factors (e.g., anxiety, hope) underlying placebo responses were provided.

Conclusions:

• State-of-the-art NLP techniques address limitations in interpretability and context sensitivity.
• LLMs link language and psychological states for quantitative exploration of biopsychosocial phenomena.
• This approach enhances understanding of how language relates to treatment outcomes, particularly placebo effects.