Objective This systematic review and meta-analysis aimed to compare endoscopic discectomy (ED) with microdiscectomy (MD) for lumbar disc herniation, evaluating patient-reported outcomes, perioperative parameters, and complications to determine if ED could replace MD as the gold standard.
Methods Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines, we searched PubMed, Embase, Scopus, and Web of Science (January 2000–June 2025) for randomized controlled trials (RCTs) and prospective cohort studies comparing MD with ED subtypes (transforaminal endoscopic lumbar discectomy [TELD], interlaminar endoscopic lumbar discectomy [IELD], and unilateral biportal endoscopy [UBE]). Outcomes included Oswestry Disability Index (ODI), visual analogue scale (VAS) for pain, operative time, hospital stay, complications, and recurrence. Pooled mean differences and odds ratios (ORs) were calculated using random-effects models, with subgroup analyses by ED subtype. Risk of bias was assessed using RoB 2.0 and ROBINS-I tools.
Results Seventeen studies (9 RCTs, 8 cohorts; n=3,115) were included. ED significantly reduced hospital stay (mean difference, -2.43 days; 95% CI, -3.62 to -1.23; p<0.05) and showed greater short-term ODI improvement (mean difference, 2.13; 95% CI, 0.58–3.67). No differences were observed in operative time, long-term ODI, or VAS scores. ED had lower wound complications but a higher recurrence risk with TELD (OR, ~2.0). High heterogeneity (I²>95%) and limited long-term data (>2 years) were noted.
Conclusion ED offers perioperative advantages and comparable efficacy but does not surpass MD due to TELD’s increased recurrence risk. IELD and UBE are promising alternatives, but MD remains the benchmark. Long-term RCTs are needed.
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Letter to Editor: Practice preference of revision surgery for recurrent lumbar disc herniation: an international survey of AO spine members Borriwat Santipas, Jin-Sung Kim European Spine Journal.2026;[Epub] CrossRef
Mohamed A.R. Soliman, Hendrick Francois, Alexander O. Aguirre, Asham Khan, Waeel Hamouda, Stipe Ćorluka, Zorica Buser, Samuel K. Cho, S. Tim Yoon, AO Spine Knowledge Forum Degenerative
Neurospine 2026;23(1):42-58. Published online January 31, 2026
Objective Lumbar discectomy is one of the most frequently undertaken procedures for the management of lumbar disc herniation. However, it may be complicated by recurrent disc herniation, with reported rates as high as 25%. To the authors’ knowledge, this study is the largest systematic review to date, analyzing the clinical and radiographic risk factors for recurrent disc herniation.
Methods A systematic literature search of Embase and PubMed/Medline, covering the period from inception to October 1, 2025, was conducted to identify case-control or cohort studies reporting risk factors for recurrent disc herniation. Risk factors were classified into baseline, clinical, and radiographic risk factors. Meta-analysis was performed for any reported risk factor with data from 3 or more studies. The assessment included an evaluation of publication bias and heterogeneity.
Results A total of 51 studies published during the search timeframe, comprising 52,479 patients, met the inclusion criteria. Recurrent disc herniation occurred in 6,794 patients (12.9%). Significant risk factors for disc herniation included high body mass index (BMI) (standard mean difference [SMD], 0.48; 95% confidence interval [CI], 0.26–0.70), diabetes (odds ratio [OR], 1.48; 95% CI, 1.23–1.77), increased sagittal range of motion (SMD, 2.15; 95% CI, 0.35–3.94), and Modic changes (OR, 2.97; 95% CI, 2.20–4.01). No other significant predictors for recurrent disc herniation were identified.
Conclusion In conclusion, patients with high BMI, diabetics, increased sagittal range of motion, and presence of Modic changes are at increased risk of recurrent disc herniation. Future prospective studies are needed to validate the risk factors identified in this study associated with recurrent disc herniation.
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Comparison of clinical efficacy and minimal invasiveness between unilateral biportal endoscopic and percutaneous endoscopic lumbar discectomy in the treatment of calcified lumbar disc herniation: a retrospective analysis Zhifeng Cheng, Lei Sun, Tao Tang, Qiang Wu, Likan Liang, Haonan Lu, Hao Xu, Bo Hu BMC Musculoskeletal Disorders.2026;[Epub] CrossRef
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Luca Ambrosio, Sathish Muthu, Samuel K. Cho, Micheal S. Virk, Juan P. Cabrera, Patrick C. Hsieh, Andreas K. Demetriades, Stipe Ćorluka, S. Tim Yoon, Gianluca Vadalà, AO Spine Knowledge Forum Degenerative
Neurospine 2025;22(1):40-47. Published online March 31, 2025
Objective This study aims to assess global trends in the use of open surgery versus minimally invasive surgery (MIS) for the treatment of single-level L4–5 degenerative lumbar spondylolisthesis (DLS).
Methods A cross-sectional online survey issued by the AO Spine Knowledge Forum Degenerative was conducted among AO Spine members between July and September 2023. Participants were presented with 3 clinical cases of L4–5 grade 1 DLS, each with varying degrees of stenosis and instability. The survey captured surgeon demographics and preferences for open versus MIS approaches. Statistical analysis, including chi-square tests and logistic regression, was performed to explore associations between surgical choices and surgeon demographics.
Results A total of 943 surgeons responded, with 479 completing the survey. Open surgery was the preferred approach in all 3 cases (58.8%, 57.3%, and 42.4%, respectively), particularly in cases involving central and bilateral foraminal stenosis. MIS was the second most common choice, particularly for unilateral foraminal stenosis with mild instability (38.8%). Surgeons’ preferences varied significantly by region, age, and fellowship training, with younger and fellowship-trained surgeons more likely to prefer MIS.
Conclusion The study highlights the continued predominance of open surgery for DLS, especially in complex cases, despite the growing acceptance of MIS. Significant regional and demographic variations in surgical preferences suggest the need for tailored guidelines and standardized training protocols to optimize patient outcomes. Future research should focus on the long-term efficacy of these approaches and the impact of evolving technologies on surgical decision-making.
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Objective Biportal endoscopic transforaminal lumbar interbody fusion (BE-TLIF) is an emerging, minimally invasive technique performed under biportal endoscopic guidance. However, concerns regarding cage subsidence and sufficient fusion during BE-TLIF necessitate careful selection of an appropriate interbody cage to improve surgical outcomes. This study compared the fusion rate, subsidence, and other radiographic parameters according to the material and size of the cages used in BE-TLIF.
Methods In this retrospective cohort study, patients who underwent single-segment BE-TLIF between April 2019 and February 2023 were divided into 3 groups: group A, regular-sized three-dimensionally (3D)-printed titanium cages; group B, regular-sized polyetheretherketone cages; and group C, large-sized 3D-printed titanium cages. Radiographic parameters, including lumbar lordosis, segmental lordosis, anterior and posterior disc heights, disc angle, and foraminal height, were measured before and after surgery. The fusion rate and severity of cage subsidence were compared between the groups.
Results No significant differences were noted in the demographic data or radiographic parameters between the groups. The fusion rate on 1-year postoperative computed tomography was comparable between the groups. The cage subsidence rate was significantly lower in group C than in group A (41.9% vs. 16.7%, p=0.044). The severity of cage subsidence was significantly lower in group C (0.93±0.83) than in groups A (2.20±1.84, p=0.004) and B (1.79±1.47, p=0.048).
Conclusion Cage materials did not affect the 1-year postoperative outcomes of BE-TLIF; however, subsidence was markedly reduced in large cages. Larger cages may provide more stable postoperative segments.
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Neurospine 2024;21(1):128-146. Published online March 31, 2024
Objective Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT’s 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines.
Methods ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy.
Results Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT’s GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response.
Conclusion ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model’s performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model’s responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.
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Neurospine 2024;21(1):204-211. Published online March 31, 2024
Objective To evaluate the global practice pattern of wound dressing use after lumbar fusion for degenerative conditions.
Methods A survey issued by AO Spine Knowledge Forums Deformity and Degenerative was sent out to AO Spine members. The type of postoperative dressing employed, timing of initial dressing removal, and type of subsequent dressing applied were investigated. Differences in the type of surgery and regional distribution of surgeons’ preferences were analyzed.
Results Right following surgery, 60.6% utilized a dry dressing, 23.2% a plastic occlusive dressing, 5.7% glue, 6% a combination of glue and polyester mesh, 2.6% a wound vacuum, and 1.2% other dressings. The initial dressing was removed on postoperative day 1 (11.6%), 2 (39.2%), 3 (20.3%), 4 (1.7%), 5 (4.3%), 6 (0.4%), 7 or later (12.5%), or depending on drain removal (9.9%). Following initial dressing removal, 75.9% applied a dry dressing, 17.7% a plastic occlusive dressing, and 1.3% glue, while 12.1% used no dressing. The use of no additional coverage after initial dressing removal was significantly associated with a later dressing change (p < 0.001). Significant differences emerged after comparing dressing management among different AO Spine regions (p < 0.001).
Conclusion Most spine surgeons utilized a dry or plastic occlusive dressing initially applied after surgery. The first dressing was more frequently changed during the first 3 postoperative days and replaced with the same type of dressing. While dressing policies tended not to vary according to the type of surgery, regional differences suggest that actual practice may be based on personal experience rather than available evidence.
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Neurospine 2024;21(1):149-158. Published online January 31, 2024
Objective Large language models like chat generative pre-trained transformer (ChatGPT) have found success in various sectors, but their application in the medical field remains limited. This study aimed to assess the feasibility of using ChatGPT to provide accurate medical information to patients, specifically evaluating how well ChatGPT versions 3.5 and 4 aligned with the 2012 North American Spine Society (NASS) guidelines for lumbar disk herniation with radiculopathy.
Methods ChatGPT's responses to questions based on the NASS guidelines were analyzed for accuracy. Three new categories—overconclusiveness, supplementary information, and incompleteness—were introduced to deepen the analysis. Overconclusiveness referred to recommendations not mentioned in the NASS guidelines, supplementary information denoted additional relevant details, and incompleteness indicated omitted crucial information from the NASS guidelines.
Results Out of 29 clinical guidelines evaluated, ChatGPT-3.5 demonstrated accuracy in 15 responses (52%), while ChatGPT-4 achieved accuracy in 17 responses (59%). ChatGPT-3.5 was overconclusive in 14 responses (48%), while ChatGPT-4 exhibited overconclusiveness in 13 responses (45%). Additionally, ChatGPT-3.5 provided supplementary information in 24 responses (83%), and ChatGPT-4 provided supplemental information in 27 responses (93%). In terms of incompleteness, ChatGPT-3.5 displayed this in 11 responses (38%), while ChatGPT-4 showed incompleteness in 8 responses (23%).
Conclusion ChatGPT shows promise for clinical decision-making, but both patients and healthcare providers should exercise caution to ensure safety and quality of care. While these results are encouraging, further research is necessary to validate the use of large language models in clinical settings.
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Objective Subsidence following anterior cervical discectomy and fusion (ACDF) may lead to disruptions of cervical alignment and lordosis. The purpose of this study was to evaluate the effect of subsidence on segmental, regional, and global lordosis.
Methods This was a retrospective cohort study performed between 2016–2021 at a single institution. All measurements were performed using lateral cervical radiographs at the immediate postoperative period and at final follow-up greater than 6 months after surgery. Associations between subsidence and segmental lordosis, total fused lordosis, C2–7 lordosis, and cervical sagittal vertical alignment change were determined using Pearson correlation and multivariate logistic regression analyses.
Results One hundred thirty-one patients and 244 levels were included in the study. There were 41 one-level fusions, 67 two-level fusions, and 23 three-level fusions. The median follow-up time was 366 days (interquartile range, 239–566 days). Segmental subsidence was significantly negatively associated with segmental lordosis change in the Pearson (r = -0.154, p = 0.016) and multivariate analyses (beta = -3.78; 95% confidence interval, -7.15 to -0.42; p = 0.028) but no associations between segmental or total fused subsidence and any other measures of cervical alignment were observed.
Conclusion We found that subsidence is associated with segmental lordosis loss 6 months following ACDF. Surgeons should minimize subsidence to prevent long-term clinical symptoms associated with poor cervical alignment.
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Developments in machine learning in recent years have precipitated a surge in research on the applications of artificial intelligence within medicine. Machine learning algorithms are beginning to impact medicine broadly, and the field of spine surgery is no exception. Electronic medical records are a key source of medical data that can be leveraged for the creation of clinically valuable machine learning algorithms. This review examines the current state of machine learning using electronic medical records as it applies to spine surgery. Studies across the electronic medical record data domains of imaging, text, and structured data are reviewed. Discussed applications include clinical prognostication, preoperative planning, diagnostics, and dynamic clinical assistance, among others. The limitations and future challenges for machine learning research using electronic medical records are also discussed.
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The aim to find the perfect biomaterial for spinal implant has been the focus of spinal research since the 1800s. Spinal surgery and the devices used therein have undergone a constant evolution in order to meet the needs of surgeons who have continued to further understand the biomechanical principles of spinal stability and have improved as new technologies and materials are available for production use. The perfect biomaterial would be one that is biologically inert/compatible, has a Young’s modulus similar to that of the bone where it is implanted, high tensile strength, stiffness, fatigue strength, and low artifacts on imaging. Today, the materials that have been most commonly used include stainless steel, titanium, cobalt chrome, nitinol (a nickel titanium alloy), tantalum, and polyetheretherketone in rods, screws, cages, and plates. Current advancements such as 3-dimensional printing, the ProDisc-L and ProDisc-C, the ApiFix, and the Mobi-C which all aim to improve range of motion, reduce pain, and improve patient satisfaction. Spine surgeons should remain vigilant regarding the current literature and technological advancements in spinal materials and procedures. The progression of spinal implant materials for cages, rods, screws, and plates with advantages and disadvantages for each material will be discussed.
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Objective Machine learning algorithms excel at leveraging big data to identify complex patterns that can be used to aid in clinical decision-making. The objective of this study is to demonstrate the performance of machine learning models in predicting postoperative complications following anterior cervical discectomy and fusion (ACDF).
Methods Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), and random forest decision tree (RF) models were trained on a multicenter data set of patients undergoing ACDF to predict surgical complications based on readily available patient data. Following training, these models were compared to the predictive capability of American Society of Anesthesiologists (ASA) physical status classification.
Results A total of 20,879 patients were identified as having undergone ACDF. Following exclusion criteria, patients were divided into 14,615 patients for training and 6,264 for testing data sets. ANN and LR consistently outperformed ASA physical status classification in predicting every complication (p < 0.05). The ANN outperformed LR in predicting venous thromboembolism, wound complication, and mortality (p < 0.05). The SVM and RF models were no better than random chance at predicting any of the postoperative complications (p < 0.05).
Conclusion ANN and LR algorithms outperform ASA physical status classification for predicting individual postoperative complications. Additionally, neural networks have greater sensitivity than LR when predicting mortality and wound complications. With the growing size of medical data, the training of machine learning on these large datasets promises to improve risk prognostication, with the ability of continuously learning making them excellent tools in complex clinical scenarios.
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