Developing a shortened spine functional index (SFI-10) for patients with sub-acute/chronic spinal disorders: a cross-sectional study.

BACKGROUND: Brief whole-spine patient-reported outcome measures (PROMs) provide regional solutions and future directions for quantifying functional status, evidence, and effective interventions. The whole-spine regional Spine Functional Index (SFI-25) is used internationally in clinical and scientific contexts to assess general sub-acute/chronic spine populations. However, to improve structural validity and practicality a shortened version is recommended. This study developed a shortened-SFI from the determined optimal number of item questions that: correlated with criteria PROMs being highly with whole-spine, moderately with regional-spine, condition-specific and patient-specific, and moderately-low with general-health and pain; retained one-dimensional structural validity and high internal consistency; and improved practicality to reduce administrative burden. METHODS: A cross-sectional study (n = 505, age = 18-87 yrs., average = 40.3 ± 10.1 yrs) of sub-acute/chronic spine physiotherapy outpatients from an international sample of convenience. Three shortened versions of the original SFI-25 were developed using 1) qualitative 'content-retention' methodology, 2) quantitative 'factorial' methodology, and 3) quantitative 'Rasch' methodology, with a fourth 'random' version produced as a comparative control. The clinimetric properties were established for structural validity with exploratory (EFA) and confirmatory (CFA) factorial analysis, and Rasch analysis. Criterion validity used the: whole-spine SFI-25 and Functional Rating Index (FRI); regional-spine Neck Disability Index (NDI), Oswestry Disability Index (ODI), and Roland Morris Questionnaire (RMQ), condition-specific Whiplash Disability Questionnaire (WDQ); and patient-specific functional scale (PSFS); and determined floor/ceiling effect. A post-hoc pooled international sub-acute/chronic spine sample (n = 1433, age = 18-91 yrs., average = 42.0 ± 15.7 yrs) clarified the findings and employed the general-health EuroQuol-Index (EQ-5D), and 11-point Pain Numerical Rating Scale (P-NRS) criteria. RESULTS: A 10-item SFI retained structural validity with optimal practicality requiring no computational aid. The SFI-10 concept-retention-version demonstrated preferred criterion validity with whole-spine criteria (SFI-25 = 0.967, FRI = 0.810) and exceeded cut-off minimums with regional-spine, condition-specific, and patient-specific measures. An unequivocal one-dimensional structure was determined. Internal consistency was satisfactory (α = 0.80) with no floor/ceiling effect. Post-hoc analysis of the international sample confirmed these findings. CONCLUSION: The SFI-10 qualitative concept-retention version was preferred to quantitative factorial and Rasch versions, demonstrated structural and criterion validity, and preferred correlation with criteria measures. Further longitudinal research is required for reliability, error, and responsiveness, plus an examination of the practical characteristics of readability and administrative burden.

[Pediatric neurosurgical treatment of aneurysmal bone cyst at the level of thoracic spine]. / Aneurysmás csontcysta gyermek-idegsebészeti megoldása a háti gerinc magasságában.

Aneurysmal bone cysts are benign but locally aggressive bone tumours, most often affecting children and young adults. In this case report, we present the clinical  picture of a 15-year-old boy with progressive, chronic back pain. An MRI of thoracic spine  confirmed a T2 cystic spinal tumour. After considering potential options surgical removal was our choice and gross total removal was achieved with T1-3 short-segment fixation. Aneurysmal bone cysts are often rapidly expanding lesions with vascular  transformation. In order to avoid irreversible damage, in addition to early diagnosis, it is necessary to carefully consider the therapeutic options, perform surgical removal and stabilization as necessary. In case of the presented patient, extensive surgical removal and short-segmentation were performed. At 18 months of follow-up, he had no complaints and was asymptomatic. Follow-up imaging studies showed no residual or recurrent tumour to date.

Daily Pain Prediction Using Smartphone Speech Recordings of Patients With Spine Disease.

BACKGROUND: Pain evaluation remains largely subjective in neurosurgical practice, but machine learning provides the potential for objective pain assessment tools. OBJECTIVE: To predict daily pain levels using speech recordings from personal smartphones of a cohort of patients with diagnosed neurological spine disease. METHODS: Patients with spine disease were enrolled through a general neurosurgical clinic with approval from the institutional ethics committee. At-home pain surveys and speech recordings were administered at regular intervals through the Beiwe smartphone application. Praat audio features were extracted from the speech recordings to be used as input to a K-nearest neighbors (KNN) machine learning model. The pain scores were transformed from a 0 to 10 scale to low and high pain for better discriminative capacity. RESULTS: A total of 60 patients were enrolled, and 384 observations were used to train and test the prediction model. Using the KNN prediction model, an accuracy of 71% with a positive predictive value of 0.71 was achieved in classifying pain intensity into high and low. The model showed 0.71 precision for high pain and 0.70 precision for low pain. Recall of high pain was 0.74, and recall of low pain was 0.67. The overall F1 score was 0.73. CONCLUSION: Our study uses a KNN to model the relationship between speech features and pain levels collected from personal smartphones of patients with spine disease. The proposed model is a stepping stone for the development of objective pain assessment in neurosurgery clinical practice.

Are We Considering the Whole Patient? The Impact of Physical and Mental Health on the Outcomes of Spine Care.

STUDY DESIGN: Retrospective, observational. OBJECTIVE: To evaluate the influence of baseline health status on the physical and mental health (MH) outcomes of spine patients. SUMMARY OF BACKGROUND DATA: Spine conditions can have a significant burden on both the physical and MH of patients. To date, few studies have evaluated the outcomes of both dimensions of health, particularly in nonoperative populations. MATERIALS AND METHODS: At their first visit to a multidisciplinary spine clinic, 2668 nonoperative patients completed the Patient-reported Outcomes Measurement Information System-Global Health (PROMIS-GH) instrument and a questionnaire evaluating symptoms and goals of care. Patients were stratified by their baseline percentile score of the MH and physical health (PH) components of the PROMIS-GH. Four groups of patients were compared based on the presence or absence of bottom quartile PH or MH scores. The primary end point was the achievement of a minimal clinically important difference (MCID) on the MH or PH components at follow-up. Multivariate regression assessed the predictors of MCID achievement. RESULTS: After controlling for demographics, symptoms, and goals, each 1-point increase in baseline PROMIS-GH mental score reduced the odds of achieving MH MCID by 9.0% ( P <0.001). Conversely, each 1-point increase in baseline GH-physical score increased the odds of achieving MCID by 4.5% ( P =0.005). Each 1-point increase in baseline GH-physical score reduced the odds of achieving PH MCID by 12.5% ( P <0.001), whereas each 1-point increase in baseline GH-mental score increased the odds of achieving MCID by 5.0% ( P <0.001). CONCLUSIONS: Spine patients presenting with the lowest levels of physical or MH were most likely to experience clinically significant improvement in those domains. However, lower levels of physical or mental health made it less likely that patients would experience significant improvement in the alternative domain. Physicians should evaluate and address the complex spine population holistically to maximize improvement in both physical and mental health status.

Spine Surgeon Assessments of Patient Psychological Distress are Inaccurate and Bias Treatment Recommendations.

STUDY DESIGN: Prospective cohort study. OBJECTIVE: This study determined (1) the surgeon accuracy of psychological assessment in patients in spine clinic, (2) the impact of psychological distress on surgical recommendation, and (3) the correlation between patient-reported disability and psychological distress. SUMMARY OF BACKGROUND DATA: Psychological distress is common among patients presenting for spine surgery. Surgeon estimations of patients' distress may influence treatment recommendations, but little is known as to whether these assessments accurately mirror patient-perceived psychological distress. MATERIALS AND METHODS: A sample of new patients was recruited from an academic spine center. Prior to their initial consultation, patients completed the Modified Somatic Perception Questionnaire and Zung Depression Index to assess mental and physical manifestations of distress, which generated a Distress and Risk Assessment Method (DRAM) score of N (normal), R (at risk), or D (distressed). The Oswestry Disability Index and Neck Disability Index scores were also collected. Surgeons provided their estimates of the DRAM score after the visit and indicated their surgery recommendation. RESULTS: Of 296 patients, 40.5% reported some level of psychological distress (DRAM=R) and 15.9% had a high level of distress (DRAM=D). All three surgeons' ability to accurately assess the participant DRAM score was poor, with an overall kappa of 0.13 (0.08-0.18), biased toward underestimating the patient's true level of psychological distress. Patients rated as normal (N) by the surgeon were 3.78 times more likely to be recommended for surgery compared to those assessed as distressed (D) ( P =0.007). Patients with higher DRAM scores had higher Oswestry Disability Index ( P =0.008) and Neck Disability Index ( P =0.005) scores compared to those with lower DRAM scores. CONCLUSIONS: Spine surgeons have limited ability to detect psychological profiles in patients, with a tendency to underestimate levels of distress. The finding that these inaccurate assessments influence surgical recommendations underscores the importance of limiting surgeon bias in the decision-making process. LEVEL OF EVIDENCE: Diagnostic Level 2.

Management peculiarities of costovertebral hydatidosis.

Background: Costovertebral hydatidosis is a rarely reported clinical and radiological entity, estimated at less than 1% of thoracic hydatid locations. Its management is still not codified. Objective: The aim of our study was to specify the management peculiarities of costovertebral hydatidosis. Methods: Between January 2000 and December 2018, 14 patients were managed for costovertebral hydatidosis in a thoracic surgery department. Results: The mean age of our patients was 48 years. The history of a prior hydatid disease was found in 7 patients. Imaging features were suggestive in 13 cases. They showed: involvement of the spinal canal (6 cases), of the soft tissues (5 cases) and spinal cord compression (3 cases). Costovertebral resection of the hydatid lesions was complete in 12 cases. Four patients presented postoperative complications. Conclusion: Costovertebral hydatid involvement, may threaten the functional and vital prognosis. Therefore, early diagnosis and management are mandatory, before the occurrence of irreversible neurological impairment. Surgical resection remains the treatment of choice and must be complete whenever possible. Relapse is frequent, hence the importance of a regular follow-up.

The Utility of Transcranial Stimulated Motor-Evoked Potential Alerts in Cervical Spine Surgery Varies Based on Preoperative Motor Status.

STUDY DESIGN: A prospective multicenter observational study. OBJECTIVE: The aim was to investigate the validity of transcranial motor-evoked potentials (Tc-MEP) in cervical spine surgery and identify factors associated with positive predictive value when Tc-MEP alerts are occurred. SUMMARY OF BACKGROUND DATA: The sensitivity and specificity of Tc-MEP for detecting motor paralysis are high; however, false-positives sometimes occur. MATERIALS AND METHODS: The authors examined Tc-MEP in 2476 cases of cervical spine surgeries and compared patient backgrounds, type of spinal disorders, preoperative motor status, surgical factors, and the types of Tc-MEP alerts. Tc-MEP alerts were defined as an amplitude reduction of more than 70% from the control waveform. Tc-MEP results were classified into two groups: false-positive and true-positive, and items that showed significant differences were extracted by univariate analysis and detected by multivariate analysis. RESULTS: Overall sensitivity was 66% (segmental paralysis: 33% and lower limb paralysis: 95.8%) and specificity was 91.5%. Tc-MEP outcomes were 33 true-positives and 233 false-positives. Positive predictive value of general spine surgery was significantly higher in cases with a severe motor status than in a nonsevere motor status (19.5% vs . 6.7%, P =0.02), but not different in high-risk spine surgery (20.8% vs . 19.4%). However, rescue rates did not significantly differ regardless of motor status (48% vs . 50%). In a multivariate logistic analysis, a preoperative severe motor status [ P =0.041, odds ratio (OR): 2.46, 95% confidence interval (95% CI): 1.03-5.86] and Tc-MEP alerts during intradural tumor resection ( P <0.001, OR: 7.44, 95% CI: 2.64-20.96) associated with true-positives, while Tc-MEP alerts that could not be identified with surgical maneuvers ( P =0.011, OR: 0.23, 95% CI: 0.073-0.71) were associated with false-positives. CONCLUSION: The utility of Tc-MEP in patients with a preoperative severe motor status was enhanced, even in those without high-risk spine surgery. Regardless of the motor status, appropriate interventions following Tc-MEP alerts may prevent postoperative paralysis.

A Pilot Study Implementing a Machine Learning Algorithm to Use Artificial Intelligence to Diagnose Spinal Conditions.

BACKGROUND: Chronic spinal pain is the most prevalent chronic disease, with chronic persistent spinal pain lasting longer than one-year reported in 25% to 60% of the patients. Health care expenditures have been escalating and the financial impact on the US economy is growing. Among multiple modalities of treatments available, facet joint interventions and epidural interventions are the most common ones, in addition to surgical interventions and numerous other conservative modalities of treatments. Despite these increasing costs in the diagnosis and management, disability continues to increase. Consequently, algorithmic approaches have been described as providing a disciplined approach to the use of spinal interventional techniques in managing spinal pain. This approach includes evaluative, diagnostic, and therapeutic approaches, which avoids unnecessary care, as well as poorly documented practices.  Recently, techniques involving artificial intelligence and machine learning have been demonstrated to contribute to the improved understanding, diagnosis, and management of both acute and chronic disease in line with well-designed algorithmic approach. The use of artificial intelligence and machine-learning techniques for the diagnosis of spinal pain has not been widely investigated or adopted. OBJECTIVES: To evaluate whether it is possible to use artificial intelligence via machine learning algorithms to analyze specific data points and to predict the most likely diagnosis related to spinal pain. STUDY DESIGN: This was a prospective, observational pilot study. SETTING: A single pain management center in the United States. METHODS: A total of 246 consecutive patients with spinal pain were enrolled. Patients were given an iPad to complete a Google form with 85 specific data points, including demographic information, type of pain, pain score, pain location, pain duration, and functional status scores. The data were then input into a decision tree machine learning software program that attempted to learn which data points were most likely to correspond to the practitioner-assigned diagnosis. These outcomes were then compared with the practitioner-assigned diagnosis in the chart. RESULTS: The average age of the included patients was 57.4 years (range, 18-91 years). The majority of patients were women and the average pain history was approximately 2 years. The most common practitioner-assigned diagnoses included lumbar radiculopathy and lumbar facet disease/spondylosis. Comparison of the software-predicted diagnosis based on reported symptoms with practitioner-assigned diagnosis revealed that the software was accurate approximately 72% of the time. LIMITATIONS: Additional studies are needed to expand the data set, confirm the predictive ability of the data set, and determine whether it is broadly applicable across pain practices. CONCLUSIONS: Software-predicted diagnoses based on the data from patients with spinal pain had an accuracy rate of 72%, suggesting promise for augmented decision making using artificial intelligence in this setting.

Is It the Back or the Hip? Differentiating Lumbar Spine From Hip Pathologies: Key Points of Evaluation and Treatment.

Disorders of the hip and spine commonly coexist and are difficult to disentangle. When they do occur together, the pathology is often referred to as hip-spine syndrome. When hip-spine syndrome is suspected, it is critically important to properly identify the relative contributions that the hip and spine each provide to a patient's overall clinical presentation. To focus on the incorrect anatomic site would be a disservice to the patient. The interconnectivity of hip and spine pathology, the various clinical presentations of the most commonly seen hip and spine disorders, the broad differential and suggested workup that should be considered for such patients, the various treatment modalities available, and the clinical predictors and expected outcomes for patients with hip-spine syndrome are important factors for review.

Update on the Diagnosis and Management of Spinal Infections.

Pyogenic spinal infections are uncommon, but their incidence has increased. Diagnosis is based on clinical, laboratory, and imaging findings. Delayed diagnosis occurs frequently and can lead to poor outcomes. Early radiographic findings are nonspecific; MRI is the best imaging study for diagnosis. The goal of treatment is to eradicate infection, prevent recurrence, preserve spinal stability, avoid deformity, relieve pain, and prevent or reverse neurologic deficit. Current guidelines recommend antibiotics be administered for 6 weeks if there is resolution of symptoms and normalization of inflammatory parameters. Surgery is required in patients with neurologic deficit, uncontrolled sepsis, spinal instability, deformity, and failure of medical treatment and to manage epidural abscess. Classic treatment of epidural abscess is surgical, but recent studies have challenged this approach. Surgical techniques used to manage these infections are varied; they include anterior, posterior, and combined approaches, and minimally invasive surgery. Current management has decreased mortality; however, the prognosis is affected by treatment failure, recurrent infection, or potential of persistent disability secondary to deformity, chronic pain, or permanent neurologic impairment.

Research progress of diagnosing methodology for lumbar segmental instability: A narrative review.

OBJECTIVE: Lumbar segmental instability (LSI) is due to a pathologic movement of the vertebral body on the vertebra below and often causes clinical symptoms. The study was to achieve the research progress of diagnosing methodology for lumbar segmental instability and help clinicians make treatment choices. METHODS: The data for this study were collected from the MEDLINE, Springer, Web of Science, PubMed, EMBASE, the Cochrane Central Register of Controlled Trials, Evidence Based Medicine Reviews, VIP, and CNKI. The search terms were integrated as follows: "(∗lumbar instability∗ OR ∗lumbar spondylolisthesis∗) and (∗image∗ or ∗diagnosis∗)". Studies without clear radiographic instable criteria, case reports, letter, and basic research were excluded. RESULT: In total, 39 articles published met our inclusion criteria. The various modalities were used to diagnosis LSI in these studies included radiographs, facet joint degeneration and physical examination tests. CONCLUSION: Overall, there have been a variety of researches to develop the diagnosing methodology for LSI, and many have been successful, although no consensus has been reached yet. However, it is believed that the diagnosis of LSI will become easier and more accurate in the near future.

Utility of machine learning algorithms in degenerative cervical and lumbar spine disease: a systematic review.

Machine learning is a rapidly evolving field that offers physicians an innovative and comprehensive mechanism to examine various aspects of patient data. Cervical and lumbar degenerative spine disorders are commonly age-related disease processes that can utilize machine learning to improve patient outcomes with careful patient selection and intervention. The aim of this study is to examine the current applications of machine learning in cervical and lumbar degenerative spine disease. A systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A search of PubMed, Embase, Medline, and Cochrane was conducted through May 31st, 2020, using the following terms: "artificial intelligence" OR "machine learning" AND "neurosurgery" AND "spine." Studies were included if original research on machine learning was utilized in patient care for degenerative spine disease, including radiographic machine learning applications. Studies focusing on robotic applications in neurosurgery, navigation, or stereotactic radiosurgery were excluded. The literature search identified 296 papers, with 35 articles meeting inclusion criteria. There were nine studies involving cervical degenerative spine disease and 26 studies on lumbar degenerative spine disease. The majority of studies for both cervical and lumbar spines utilized machine learning for the prediction of postoperative outcomes, with 5 (55.6%) and 15 (61.5%) studies, respectively. Machine learning applications focusing on degenerative lumbar spine greatly outnumber the current volume of cervical spine studies. The current research in lumbar spine also demonstrates more advanced clinical applications of radiographic, diagnostic, and predictive machine learning models.

The Hip-Spine Challenge.

➤: The proper diagnosis and treatment of patients with concurrent hip and spine pathological processes can be challenging because of the substantial overlap in symptomatology. ➤: There is no consensus on which pathological condition should be addressed first. ➤: Factors such as advanced spinal degeneration, deformity, and prior fusion alter the biomechanics of the spinopelvic unit. Attention should be paid to recognizing these issues during the work-up for a total hip arthroplasty as they can result in an increased risk of dislocation. ➤: In patients with concurrent spine and hip degeneration, the surgeon must pay close attention to appropriate implant positioning and have consideration for implants with enhanced stability to minimize the risk of dislocation. ➤: A proper understanding of sagittal balance and restoration of this balance is integral to improving patient outcomes following spinal surgery. ➤: The advent of new imaging modalities, increased awareness of spinopelvic mobility, as well as a better understanding of sagittal alignment will hopefully improve our treatment of patients with hip-spine syndrome.

The Hip-Spine Connection: How to Differentiate Hip Conditions From Spine Pathology.

Practitioners treating spine pathology commonly encounter patients with hip pain. These patients frequently manifest groin, buttock, trochanteric, or knee pain. Complex biomechanical interaction between the axial and the appendicular skeleton may result in compensatory adaptation. Cross-innervation of the hip and surrounding tissues may make diagnosis of the primary source difficult, leading to delayed diagnosis. This review elucidates the interaction between the hip and the spine and can assist clinicians in recognizing the hip as a potential source of pain. Diagnostic guidelines enabling a streamlined workup of patients presenting with confusing symptoms related to the hip that masquerade as a spine disorder are presented. [Orthopedics. 2021;44(6):e699-e706.].

The fatty degeneration of the lumbar erector spinae muscles affects dynamic spinal compensation ability during gait in adult spinal deformity.

This study aimed to investigate whether fat infiltration in lumbar paravertebral muscles assessed by magnetic resonance imaging (MRI) could be related to dynamic sagittal spino-pelvic balance during gait in adult spinal deformity (ASD). This is a retrospective analysis of 28 patients with ASD. The fat infiltration rate of lumbar erector spinae muscles, multifidus muscles and psoas major muscles was measured by T2 weighted axial MRI at L1-2 and L4-5. Dynamic sagittal spinal and pelvic angles during gait were evaluated using 3D motion analysis. The correlation between fat infiltration rate of those muscles with variations in dynamic kinematic variables while walking and static radiological parameters was analyzed. Spinal kyphosis and pelvic anteversion significantly increased during gait. Fat infiltration rate of erector spinae muscles at L1-2 was positively correlated with thoracic kyphosis (r = 0.392, p = 0.039) and pelvic tilt (r = 0.415, p = 0.028). Increase of spinal kyphosis during walking was positively correlated with fat infiltration rate of erector spinae muscles both at L1-2 (r = 0.394, p = 0.038) and L4-5 (r = 0.428, p = 0.023). Qualitative evaluation of lumbar erector spinae muscles assessed by fat infiltration rate has the potential to reflect dynamic spino-pelvic balance during gait.

The diagnostic and prognostic value of artificial intelligence and artificial neural networks in spinal surgery : a narrative review.

In recent years, machine learning (ML) and artificial neural networks (ANNs), a particular subset of ML, have been adopted by various areas of healthcare. A number of diagnostic and prognostic algorithms have been designed and implemented across a range of orthopaedic sub-specialties to date, with many positive results. However, the methodology of many of these studies is flawed, and few compare the use of ML with the current approach in clinical practice. Spinal surgery has advanced rapidly over the past three decades, particularly in the areas of implant technology, advanced surgical techniques, biologics, and enhanced recovery protocols. It is therefore regarded an innovative field. Inevitably, spinal surgeons will wish to incorporate ML into their practice should models prove effective in diagnostic or prognostic terms. The purpose of this article is to review published studies that describe the application of neural networks to spinal surgery and which actively compare ANN models to contemporary clinical standards allowing evaluation of their efficacy, accuracy, and relatability. It also explores some of the limitations of the technology, which act to constrain the widespread adoption of neural networks for diagnostic and prognostic use in spinal care. Finally, it describes the necessary considerations should institutions wish to incorporate ANNs into their practices. In doing so, the aim of this review is to provide a practical approach for spinal surgeons to understand the relevant aspects of neural networks. Cite this article: Bone Joint J 2021;103-B(9):1442-1448.

Pseudarthrosis and Rod Fracture Rates After Transforaminal Lumbar Interbody Fusion at the Caudal Levels of Long Constructs for Adult Spinal Deformity Surgery.

BACKGROUND: Interbody fusion at the caudal levels of long constructs for adult spinal deformity (ASD) surgery is used to promote fusion and secure a solid foundation for maintenance of deformity correction. We sought to evaluate long-term pseudarthrosis, rod fracture, and revision rates for TLIF performed at the base of a long construct for ASD. METHODS: We reviewed 316 patients who underwent TLIF as a component of ASD surgery for medical comorbidities, surgical characteristics, and rate of unplanned reoperation for pseudarthrosis or instrumentation failure at the TLIF level. Fusion grading was assessed after revision surgery for pseudarthrosis at the TLIF level. RESULTS: Rate of pseudarthrosis at the TLIF level was 9.8% (31/316), and rate of rod fractures was 7.9% (25/316). The rate of revision surgery at the TLIF level was 8.9% (28/316), and surgery was performed at a mean of 20.4 ± 16 months from the index procedure. Current smoking status (odds ratio 3.34, P = 0.037) was predictive of pseudarthrosis at the TLIF site. At a mean follow-up of 43 ± 12 months after revision surgery, all patients had achieved bony union at the TLIF site. CONCLUSIONS: At 3-year follow-up, the rate of pseudarthrosis after TLIF performed at the base of a long fusion for ASD was 9.8%, and the rate of revision surgery to address pseudarthrosis and/or rod fracture was 8.9%. All patients were successfully treated with revision interbody fusion or posterior augmentation of the fusion mass, without need for further revision procedures at the TLIF level.