Artificial intelligence and machine learning (AI/ML) could enhance the ability to detect
patterns of clinical characteristics in low-back pain (LBP) and guide treatment. We …

Abstract Background The STarT Back strategy for categorizing and treating patients with low
back pain (LBP) improved patients' function while reducing costs in England. Objective This …

Purpose Non-specific low back pain (NLBP) causes an enormous burden to patients and
tremendous costs for health care systems worldwide. Frequently, treatments are not oriented …

Objective The purpose of this systematic review was to investigate the extent to which the
STarT Back Screening Tool (SBST) has been evaluated for (1) the quality of translation of …

Background Stratified models of care are valuable for addressing psychosocial factors which
influence the outcome of patients with musculoskeletal disorders. Introducing such models …

Purpose Non-specific low back pain (NLBP) exerts a profound impact on global health and
economics. In the era of Web 3.0, digital therapeutics offer the potential to improve NLBP …

Background Low back pain (LBP) is the most common reported musculoskeletal disorder,
with large prevalence numbers and high costs. Focus on early identification of patients at …

Background The STarT-MSK-Tool is an adaptation of the well established STarT-Back-Tool,
used to risk-stratify patients with a wider range of musculoskeletal presentations. Objective …

Objective To evaluate whether current Dutch primary care clinicians offer tailored treatment
to patients with low back pain (LBP) or neck pain (NP) according to their risk stratification …

Objective. The purpose of this study was to translate and to investigate the reliability and
validity of the STarT Back screening tool (SBT) in the primary care setting among patients …