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Objective: This study utilized the theoretical framework of the "fear avoidance model" (FAM) and investigated the role of fear of attack in pain-related disability. To this end, a measurement specific to cluster headache (CH) was used to investigate whether fear of attacks, alongside attack frequency, is a significant predictor of pain-related disability in CH., Background: Cluster headache substantially impacts daily functioning, yet empirical research exploring specific contributing factors is limited., Methods: A cross-sectional online survey was undertaken in patients with CH, gathering sociodemographic, clinical data, and responses on the Cluster Headache Scale and the Depression, Anxiety and Stress Scale., Results: Analysis of data from 640 patients (chronic CH: 287/640 [44.8%]; female: 264/640 [41.3%]; male: 373/640 [58.3%]; gender diverse: three of 640 [0.5%]; age range: 18-86 years; mean [standard deviation] Cluster Headache Scales subscale disability score: 36.9 [9.8]; out of 869 respondents) revealed that both attack frequency and fear of attacks significantly predicted pain-related disability (p < 0.001, percentage of variance explained: R 2  = 0.24). More variance was explained by fear of attacks (R 2  = 0.22) than by attack frequency (R 2  = 0.02). This relationship remained significant even when controlling for depression and anxiety, which were also identified as independent predictors of pain-related disability (p < 0.001, R 2  = 0.44)., Conclusion: This study emphasizes the relevance of psychological factors in CH-related disability. Fear of attacks was found to be an independent predictor, while attack frequency was of minor relevance. Empirical investigation of the FAM in CH could improve the understanding of the mechanisms underlying disability and contribute to the development of CH-specific interventions., (© 2024 The Author(s). Headache: The Journal of Head and Face Pain published by Wiley Periodicals LLC on behalf of American Headache Society.)

While the potential of Artificial Intelligence (AI)-particularly Natural Language Processing (NLP) models-for detecting symptoms of depression from text has been vastly researched, only a few studies examine such potential for the detection of social anxiety symptoms. We investigated the ability of the large language model (LLM) GPT-4 to correctly infer social anxiety symptom strength from transcripts obtained from semi-structured interviews. N = 51 adult participants were recruited from a convenience sample of the German population. Participants filled in a self-report questionnaire on social anxiety symptoms (SPIN) prior to being interviewed on a secure online teleconference platform. Transcripts from these interviews were then evaluated by GPT-4. GPT-4 predictions were highly correlated (r = 0.79) with scores obtained on the social anxiety self-report measure. Following the cut-off conventions for this population, an F 1 accuracy score of 0.84 could be obtained. Future research should examine whether these findings hold true in larger and more diverse datasets., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)