Researchers have been encouraged to identify characteristics that predict patients' responses to different musculoskeletal interventions. We describe a novel protocol to develop a prediction model that identifies patients with nerve-related neck and arm pain who are likely to benefit from the early introduction of neural tissue management (NTM). Prediction models for musculoskeletal treatments have usually been developed by analyzing single group study data with standard logistic regression. However, this approach has important limitations and our two step process for model development will address these limitations. Eligible patients will be aged 18 to 60 years with a minimum four week episode of non-traumatic neck and unilateral arm pain that is reproduced by a mechanical provocation test for the cervical nerve roots and median nerve. The outcome being predicted by the model is patient-reported improvement four weeks after baseline. Patients rating themselves at least 'moderately better' on a Global Rating of Change scale will be considered 'improved'. First, a randomized control trial (RCT) comparing standardized NTM to advice to remain active will determine whether this outcome represents a NTM treatment effect. The RCT has 80% power to detect a number needed to treat ≤. 3 favoring NTM (p. ≤. 0.05). Second, a comprehensive set of examination items will be analyzed with penalized logistic regression to select the best items for the prediction model. © 2011 Elsevier Inc.