Editorial: The Imperative for Integration in Lumbosacral Spinal Care

Chenlong Yang^1,2,3,4,5*Moksada Regmi^1,2,3,4,5

• ¹State Key Laboratory of Vascular Homeostasis and Remodeling, Beijing, China
• ²Department of Neurosurgery, Peking University Third Hospital, Beijing, China
• ³Center for Precision Neurosurgery and Oncology of Peking University Health Science Center, Beijing, China
• ⁴Health Science Centre, Peking University, Beijing, Beijing Municipality, China
• ⁵Henan Academy of Innovations in Medical Science (AIMS), Zhengzhou, China

the lack of long-term follow-up data may raise concerns about recurrence rates and the durability of the results 6 . Additionally, the homogeneity of the cohort (all treated at a single center) limits the generalizability of the findings. Validation in diverse populations, including those with multifocal cysts or concurrent spinal abnormalities, is essential before ANRR-MRI can be widely adopted as a standard diagnostic tool. Similarly, Chen et al.'s machine learning model for predicting 1-year postoperative recovery in lumbar disk herniation patients demonstrates the potential of artificial intelligence in personalized care 7 . However, the reliance on retrospective data from a single institution introduces inherent biases, and the model's performance metrics-while superior to other algorithms-lack transparency in feature importance. Clinicians cannot trust a "black box" without understanding which variables (e.g., preoperative pain scores, comorbidities, or socioeconomic factors) drive predictions 8,9 . Future studies must prioritize interpretability and external validation across healthcare systems to avoid perpetuating biased algorithms.Therapeutic advances, though innovative, frequently overlook cost-effectiveness and scalability. Tan et al.'s study on uniportal full-endoscopic (UFE) surgery for lumbar facet joint cysts under local anesthesia reports impressive pain relief and functional improvement in eight patients 10 . However, the study's exclusion of patients with comorbidities or multilevel pathology-common in real-world practice-questions its generalizability. Additionally, the small sample size and absence of a control group (e.g., comparing UFE to conventional open surgery) also help preclude definitive conclusions about its superiority. Similarly, deep brain stimulation (DBS) for spinal cord injury (SCI) recovery is hailed for its neuromodulatory potential, yet the reviewed preclinical and clinical evidence remains fragmented. Most studies focus on acute injury models, ignoring chronic SCI cases where neuroplasticity is diminished.Moreover, the emphasis on motor recovery overlooks potential autonomic benefits (e.g., bladder control), which are equally critical to patient quality of life. Without standardized protocols for DBS target selection, stimulation parameters, and rehabilitation integration, the therapy risks becoming a costly, unproven intervention rather than a scalable solution.Rehabilitation strategies, while foundational to care, often lack mechanistic rigor. Liu et al.'s meta-analysis advocates for suspension exercise training (SET) in lumbar disk herniation, citing significant improvements in pain and functional scores 11 . However, the high heterogeneity (I² = 86-92%) in outcomes undermines confidence in its efficacy. Subgroup analyses suggest SET combined with traditional Chinese medicine (TCM) yields better results than SET alone, yet the inclusion of TCM-a variable with its own unverified mechanismscomplicates attribution of benefits. Is the improvement due to SET, TCM, or placebo effects?The study design does not disentangle these factors. Similarly, the GIGER MD biofeedback device for neurogenic bladder in children demonstrates increased voiding capacity and reduced incontinence, but the 36-patient cohort lacks a control group, and the 6-month follow-up is insufficient to assess sustained benefits. Rehabilitation research must adopt more rigorous methodologies, including sham-controlled trials and mechanistic studies, to isolate therapeutic effects from confounding variables.Oncologic studies in this collection reveal ongoing challenges in managing aggressive pathologies. The sacral chordoma cohort analysis identifies lung metastasis as a key prognostic factor, with larger tumors and postoperative recurrence correlating with poorer survival 12 . While these findings align with existing literature, the retrospective design and reliance on radiographic diagnoses (without biopsy confirmation in all cases) introduce potential misclassification bias. Furthermore, the study does not address the role of emerging therapies, such as targeted molecular agents, in metastatic chordoma 13 . Similarly, the case report of adult lumbosacral neuroblastoma highlights the rarity of this malignancy but offers limited insight into optimal management. The decision to prioritize chemotherapy over radical resection reflects institutional bias rather than evidence-based consensus, emphasizing the need for collaborative registries to pool data and establish standardized guidelines for rare spinal tumors. Given these limitations, we came up with several recommendations. First, methodological transparency must be prioritized. Studies leveraging machine learning should publish code and datasets to enable replication. Clinical trials-whether surgical or rehabilitative-must incorporate control arms and longer follow-up periods to distinguish treatment effects from natural history. Second, cost-effectiveness analyses are non-negotiable. Innovations like UFE surgery and DBS will fail to translate into widespread practice if their benefits do not justify the economic burden on healthcare systems. Third, collaborative efforts to harmonize measures like ODI or JOA across institutions would reduce heterogeneity and enable metaanalyses with greater statistical power. Fourth, research may explicitly evaluate access barriers to advanced diagnostics (e.g., ANRR-MRI) or therapies (e.g., gene-targeted SMA drugs) in low-resource regions, rather than assuming scalability.Finally, the field must move beyond symptom-centric outcomes. While pain and functional scores are important, patient-reported outcomes-such as mental health, social participation, and caregiving burden-are rarely measured. For example, while SMA therapies like nusinersen improve motor function, their impact on familial stress or financial toxicity remains unstudied. Similarly, DBS studies for SCI focus on gait recovery but neglect bladder and bowel function, which patients often rank as higher priorities. A paradigm shift toward holistic, patient-defined endpoints is overdue.In conclusion, the studies in this Research Topic illustrate both the promise and pitfalls of contemporary lumbosacral spinal care. While technological and therapeutic advances abound, their clinical value remains uncertain without critical appraisal, methodological rigor, and a commitment to equity. Researchers must resist the allure of novelty and instead embrace stability and patient partnership to ensure that progress translates into meaningful, accessible outcomes. The path forward requires not just smarter tools, but wiser stewardship of innovation.