rehabilitation is inherently complex, requiring specialized expertise and interdisciplinary collaboration to address the intricate sensorimotor, multi-joint, and bilateral arm functions involved in meaningful, everyday activities [3]. Even when upper-limb rehabilitation is prioritized, limited staffing and resource constraints often result in therapy dosages that fall short of the intensity needed to achieve clinically meaningful improvements [5; 6]. To address these challenges, emerging technologies, such as robotic rehabilitation, are gaining attention as a potential solution to enhancing the accessibility and efficacy of upper-limb rehabilitation [7]. Robotic rehabilitation is advocated for its ability to personalize upper-limb rehabilitation tasks, promote active engagement, and deliver high dosages of task-specific training with reduced clinician involvement [8; 9; 10; 11]. Meta-analyses suggest that robotic rehabilitation is potentially as effective, if not more effective, than conventional rehabilitation, for improving upper-limb capacity [10; 11; 12]. These effects may be attributed to specific robotic device features, such as the joints being mobilized, assistance and feedback type, portability, and laterality of the device [11]. Additionally, program parameters, including rehabilitation setting, clinician and peer involvement, number of devices used, type of training, and amount of practice, may also influence outcomes [10; 11]. However, despite improvements in upper-limb capacity, robotic rehabilitation has not consistently translated to improvements in the performance of activities of daily living [8; 10; 11; 12]. This raises questions about the mechanisms by which robotic rehabilitation contributes to improved upper-limb function and which device features and program parameters most effectively promote recovery [8; 10; 11; 12]. Efforts to isolate the true effects of robotic upper-limb interventions and their conventional comparators are complicated by poor intervention reporting [8; 11; 12]. Key details regarding device features (e.g., assistance type) and program parameters (e.g., training amount, supervision level) are often poorly described or omitted from clinical trials [8; 11; 12]. Given the rapid evolution of this field, poor reporting is particularly problematic, as it perpetuates research waste [13]. Without complete and transparent reporting, person- and intervention-specific features that influence outcomes cannot be identified, compared, or optimized [13; 14; 15]. Moreover, clinicians cannot advocate for nor replicate robotic interventions if research does not provide sufficient guidance on their use [13; 14; 15]. These issues are exacerbated by the inconsistency of intervention reporting location, where key intervention details may be obscurely dispersed across various article sections (e.g., Introduction, Methods, Results, Figures, Appendix, Supplementary Files) and are not readily accessible [16]. Poor intervention reporting quality may significantly deter the optimization and clinical uptake of robotic upper-limb interventions in stroke rehabilitation [13; 14; 15; 17; 18]. Given the evidence supporting the efficacy of upper-limb robotics, its potential to augment or offer advantages over conventional rehabilitation, and the extensive time and cost being invested into optimizing their device and intervention design, it is crucial that the advancement of robotics is supported by comprehensive intervention reporting [8; 11; 12]. Addressing reporting challenges requires a systematic assessment of intervention reporting quality. The TIDieR-Rehab checklist, an extension of the original Template for Intervention Description and Replication (TIDieR) [16], was developed to evaluate the reporting of complex rehabilitation interventions and is well-suited to evaluating robotic interventions [15].