Reframing diagnostic reasoning: the Bayesian imperative in shoulder examination.

Despite the widespread use of clinical shoulder tests such as the Jobe or Neer test, diagnostic reasoning often remains binary and insufficiently quantitative. Moreover, reported diagnostic accuracy varies substantially across studies, with sensitivity, specificity, and likelihood ratios typically presented as ranges that do not adequately reflect study weighting or uncertainty. In this Perspective, we apply a Bayesian framework to formalize diagnostic reasoning by integrating pre-test probability with pooled likelihood ratios, using published data from Hegedus et al. (2012). This approach allows for the calculation of post-test probability in a transparent and reproducible manner. Using a clinical example, a pre-test probability of 30% increased to approximately 51% following a positive Drop Arm Test, illustrating that even tests with comparatively higher pooled likelihood ratios produce only moderate shifts in diagnostic probability. However, when multiple tests are applied sequentially, the combined effect results in a substantial increase in post-test probability (up to approximately 63%). Importantly, while the final post-test probability remains invariant to the order of test application due to the multiplicative nature of likelihood ratios, the intermediate diagnostic trajectory differs depending on the sequence, with implications for clinical decision-making and efficiency. These findings highlight the limited standalone diagnostic value of individual clinical tests and emphasize the importance of structured, sequential test application. Rather than relying on heterogeneous range-based summaries, Bayesian modeling provides a coherent and evidence-based framework for clinical reasoning that explicitly accounts for diagnostic uncertainty. As such, it offers a more robust approach for interpreting clinical test results in musculoskeletal care.