ABSTRACT Background Expectations shape therapeutic outcomes, yet their systematic assessment remains limited in clinical and research settings. To address this gap, we developed and validated the Brief Instrument for the Assessment of Treatment Expectations in Chronic Pain (BITEC) using Item Response Theory. Methods The study comprised four phases. (I) Twenty‐one items were generated from expectation constructs and refined to 11 through a Delphi review (≥ 80% agreement). (II) Comprehensibility was tested in 30 women with fibromyalgia, and the scale was applied to 484 chronic pain patients; items (0–10) were recoded into four categories, and IRT reduced them to nine. (III) The final version was administered to 1127 adults with chronic pain (79.3% fibromyalgia; 20.7% nociceptive/neuropathic), and latent‐class modelling defined low–high expectation cutoffs. (IV) Construct validity was assessed via discriminant analyses in calibration ( n = 1127) and validation ( n = 242) samples to evaluate whether BITEC levels differentiated diagnostic groups, pain impact and catastrophizing. (V) We developed a bedside app to support expectation‐level classification. Results The nine‐item BITEC showed discrimination between high and low expectations (AUC 0.915; 95% CI 0.897–0.933; sensitivity 79%, specificity 96%). Across both samples, BITEC demonstrated construct validity, distinguishing expectation categories based on symptom severity, catastrophizing and pain burden. Expectation levels varied across pain phenotypes, decreasing from nociceptive pain (56.1%) to fibromyalgia (42.7%) and multiple pain conditions (26.9%). Higher symptom severity was associated with higher expectations. Conclusion BITEC is a brief, reliable, theory‐grounded instrument for stratifying treatment expectations in chronic pain; applicability across treatment modalities and clinical contexts warrants further investigation. Significance Statement Expectations strongly shape therapeutic outcomes but remain difficult to measure. The BITEC, a brief IRT‐based tool, offers a reliable way to classify treatment expectations in chronic pain, supporting personalised care and improving clinical decision‐making.
Caumo et al. (Thu,) studied this question.