ChatGPT versus NASS clinical guidelines for degenerative spondylolisthesis: a comparative analysis.

BACKGROUND CONTEXT: Clinical guidelines, developed in concordance with the literature, are often used to guide surgeons' clinical decision making. Recent advancements of large language models and artificial intelligence (AI) in the medical field come with exciting potential. OpenAI's generative AI model, known as ChatGPT, can quickly synthesize information and generate responses grounded in medical literature, which may prove to be a useful tool in clinical decision-making for spine care. The current literature has yet to investigate the ability of ChatGPT to assist clinical decision making with regard to degenerative spondylolisthesis. PURPOSE: The study aimed to compare ChatGPT's concordance with the recommendations set forth by The North American Spine Society (NASS) Clinical Guideline for the Diagnosis and Treatment of Degenerative Spondylolisthesis and assess ChatGPT's accuracy within the context of the most recent literature. METHODS: ChatGPT-3.5 and 4.0 was prompted with questions from the NASS Clinical Guideline for the Diagnosis and Treatment of Degenerative Spondylolisthesis and graded its recommendations as "concordant" or "nonconcordant" relative to those put forth by NASS. A response was considered "concordant" when ChatGPT generated a recommendation that accurately reproduced all major points made in the NASS recommendation. Any responses with a grading of "nonconcordant" were further stratified into two subcategories: "Insufficient" or "Over-conclusive," to provide further insight into grading rationale. Responses between GPT-3.5 and 4.0 were compared using Chi-squared tests. RESULTS: ChatGPT-3.5 answered 13 of NASS's 28 total clinical questions in concordance with NASS's guidelines (46.4%). Categorical breakdown is as follows: Definitions and Natural History (1/1, 100%), Diagnosis and Imaging (1/4, 25%), Outcome Measures for Medical Intervention and Surgical Treatment (0/1, 0%), Medical and Interventional Treatment (4/6, 66.7%), Surgical Treatment (7/14, 50%), and Value of Spine Care (0/2, 0%). When NASS indicated there was sufficient evidence to offer a clear recommendation, ChatGPT-3.5 generated a concordant response 66.7% of the time (6/9). However, ChatGPT-3.5's concordance dropped to 36.8% when asked clinical questions that NASS did not provide a clear recommendation on (7/19). A further breakdown of ChatGPT-3.5's nonconcordance with the guidelines revealed that a vast majority of its inaccurate recommendations were due to them being "over-conclusive" (12/15, 80%), rather than "insufficient" (3/15, 20%). ChatGPT-4.0 answered 19 (67.9%) of the 28 total questions in concordance with NASS guidelines (P = 0.177). When NASS indicated there was sufficient evidence to offer a clear recommendation, ChatGPT-4.0 generated a concordant response 66.7% of the time (6/9). ChatGPT-4.0's concordance held up at 68.4% when asked clinical questions that NASS did not provide a clear recommendation on (13/19, P = 0.104). CONCLUSIONS: This study sheds light on the duality of LLM applications within clinical settings: one of accuracy and utility in some contexts versus inaccuracy and risk in others. ChatGPT was concordant for most clinical questions NASS offered recommendations for. However, for questions NASS did not offer best practices, ChatGPT generated answers that were either too general or inconsistent with the literature, and even fabricated data/citations. Thus, clinicians should exercise extreme caution when attempting to consult ChatGPT for clinical recommendations, taking care to ensure its reliability within the context of recent literature.

Robust prediction of nonhome discharge following elective anterior cervical discectomy and fusion using explainable machine learning.

PURPOSE: Predict nonhome discharge (NHD) following elective anterior cervical discectomy and fusion (ACDF) using an explainable machine learning model. METHODS: 2227 patients undergoing elective ACDF from 2008 to 2019 were identified from a single institutional database. A machine learning model was trained on preoperative variables, including demographics, comorbidity indices, and levels fused. The validation technique was repeated stratified K-Fold cross validation with the area under the receiver operating curve (AUROC) statistic as the performance metric. Shapley Additive Explanation (SHAP) values were calculated to provide further explainability regarding the model's decision making. RESULTS: The preoperative model performed with an AUROC of 0.83 ± 0.05. SHAP scores revealed the most pertinent risk factors to be age, medicare insurance, and American Society of Anesthesiology (ASA) score. Interaction analysis demonstrated that female patients over 65 with greater fusion levels were more likely to undergo NHD. Likewise, ASA demonstrated positive interaction effects with female sex, levels fused and BMI. CONCLUSION: We validated an explainable machine learning model for the prediction of NHD using common preoperative variables. Adding transparency is a key step towards clinical application because it demonstrates that our model's "thinking" aligns with clinical reasoning. Interactive analysis demonstrated that those of age over 65, female sex, higher ASA score, and greater fusion levels were more predisposed to NHD. Age and ASA score were similar in their predictive ability. Machine learning may be used to predict NHD, and can assist surgeons with patient counseling or early discharge planning.

Geometric analysis of pedicle subtraction osteotomy (PSO) for Kyphosis correction: anterior lengthening may occur at the osteotomized body as well as at the discs above and below.

OBJECTIVE: To validate the authors kyphosis correction formula for pedicle subtraction osteotomy (PSO) cases. Additionally, to use the formula to evaluate the safety of PSO by determining if there is anterior lengthening. METHODS: Twenty-two patients with primarily kyphosis corrected by PSO and with clear landmarks on preoperative and postoperative x-rays were selected. Several anatomical lines and angle measurements were utilized as depicted previously in the Vertebral Column Resection formula (see below). Two approximations were calculated: the geometric approximation (G) = (tanG°*2 + 1)*15° and the rough approximation (R) which is about the same amount of actual shortening (x), if parallel length (y) ≥ 40; twice of x, if y < 40. For each patient, the change of segmental kyphosis angle (K°) was measured and compared with G° and R°, and the correlation between each value was analyzed. RESULTS: The absolute Mean ± SE for K - G and K - R was 2.33° ± 0.34 and 6.09° ± 0.58, respectively. K - G is < 3° (p = 0.03). K - R is < 8° (p = 0.001). In other words, K was close to G and R and thus can be predicted by these approximations. Average posterior shortening, anterior shortening, and kyphosis correction at each level were 20.8 ± 2.0 mm, - 3.64 ± 1.5 mm (which equates to anterior lengthening), and 31.05° ± 2.0, respectively. Anterior lengthening occurred in 13 cases (in 4 cases, both at the body as well as at the disc above and below.) The correlation between posterior and anterior shortening was 0.03 (p = 0.88). There were 3 cage insertion cases: 1 had anterior lengthening, while 2 had anterior shortening even with the cage. CONCLUSION: This study validated the geometric and rough approximations originally used in PVCR patients, for PSO patients. Additionally, this study found that anterior lengthening may occur in PSOs usually at the discs, but occasionally at the osteotomized body.

Thoracolumbar corpectomy/spondylectomy for spinal metastasis: a pooled analysis comparing the outcome of seven different surgical approaches.

OBJECTIVE: To compare surgical outcomes between seven different approaches for thoracolumbar corpectomy/spondylectomy in the setting of spinal metastasis. METHODS: A systematic review of literature was performed including articles on corpectomy for thoracolumbar spinal metastasis. Data were extracted and sorted by surgical approach: en bloc spondylectomy (group 1), transpedicular (group 2), costotransversectomy (group 3), mini-open retropleural/retroperitoneal (group 4a), lateral extracavitary approach (group 4b), open transthoracic/transretroperitoneal (group 5), and thoracoscopic (group 6). Comparison of demographics, blood loss, directly procedure related complications, operating time, and postoperative improvement of pain. RESULTS: A total of 63 articles were included comprising data of 774 patients with various primary tumor entities. Mean age was 51.8 years, 54% of patients were female, on average 1.46 levels were treated per patient, and mean follow-up was 1.59 years. The following statistically significant findings were observed: Blood loss was lowest for the mini-open retropleural/retroperitoneal (917 ml), thoracoscopic (1107 ml) and transthoracic approach (1172 ml) versus the posterior approach groups (1633-2261 ml); directly procedure related complications were lowest for mini-open retropleural/retroperitoneal and thoracoscopic approach (0% each) versus 7-15% in the other groups; operating time was lowest in mini-open retropleural/retroperitoneal approach (184 min) versus 300-588 min in the other groups. CONCLUSION: Less invasive approaches (mini-open retropleural/retroperitoneal and thoracoscopic) not only had superior outcome in terms of blood loss and operating time, but also were shown to be safe techniques in cancer patients with low rates of procedure-related complications. These slides can be retrieved under Electronic Supplementary Material.

Relationship between sagittal balance and adjacent segment disease in surgical treatment of degenerative lumbar spine disease: meta-analysis and implications for choice of fusion technique.

STUDY DESIGN: Meta-analysis. OBJECTIVE: To conduct a meta-analysis investigating the relationship between spinopelvic alignment parameters and development of adjacent level disease (ALD) following lumbar fusion for degenerative disease. ALD is a degenerative pathology that develops at mobile segments above or below fused spinal segments. Patient outcomes are worse, and the likelihood of requiring revision surgery is higher in ALD compared to patients without ALD. Spinopelvic sagittal alignment has been found to have a significant effect on outcomes post-fusion; however, studies investigating the relationship between spinopelvic sagittal alignment parameters and ALD in degenerative lumbar disease are limited. METHODS: Six e-databases were searched. Predefined endpoints were extracted and meta-analyzed from the identified studies. RESULTS: There was a significantly larger pre-operative PT in the ALD cohort versus control (WMD 3.99, CI 1.97-6.00, p = 0.0001), a smaller pre-operative SS (WMD - 2.74; CI - 5.14 to 0.34, p = 0.03), and a smaller pre-operative LL (WMD - 4.76; CI - 7.66 to 1.86, p = 0.001). There was a significantly larger pre-operative PI-LL in the ALD cohort (WMD 8.74; CI 3.12-14.37, p = 0.002). There was a significantly larger postoperative PI in the ALD cohort (WMD 2.08; CI 0.26-3.90, p = 0.03) and a larger postoperative PT (WMD 5.23; CI 3.18-7.27, p < 0.00001). CONCLUSION: The sagittal parameters: PT, SS, PI-LL, and LL may predict development of ALD in patients' post-lumbar fusion for degenerative disease. Decision-making aimed at correcting these parameters may decrease risk of developing ALD in this cohort. These slides can be retrieved under Electronic Supplementary Material.

Proximal Junctional Kyphosis Following Spinal Deformity Surgery in the Pediatric Patient.

Proper understanding and restoration of sagittal balance is critical in spinal deformity surgery, including conditions such as adolescent idiopathic scoliosis and Scheuermann kyphosis. One potential complication following spinal reconstruction is proximal junctional kyphosis. The prevalence of proximal junctional kyphosis varies in the literature, and several patient- and surgery-related risk factors have been identified. To date, the development of proximal junctional kyphosis has not been shown to lead to a negative clinical outcome following spinal fusion for adolescent idiopathic scoliosis or Scheuermann kyphosis. Treatment options range from simple observation in asymptomatic cases to revision surgery with extension of the fusion proximally. Several techniques and technologies are emerging that seek to address and prevent proximal junctional kyphosis.

Proximal junctional kyphosis following adult spinal deformity surgery.

PURPOSE: Proximal junctional kyphosis (PJK) is a common radiographic finding following long spinal fusions. Whether PJK leads to negative clinical outcome is currently debatable. A systematic review was performed to assess the prevalence, risk factors, and treatments of PJK. METHODS: Literature search was conducted on PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials using the terms 'proximal junctional kyphosis' and 'proximal junctional failure'. Excluding reviews, commentaries, and case reports, we analyzed 33 studies that reported the prevalence rate, risk factors, and discussions on PJK following spinal deformity surgery. RESULTS: The prevalence rates varied widely from 6 to 61.7%. Numerous studies reported that clinical outcomes for patients with PJK were not significantly different from those without, except in one recent study in which adult patients with PJK experienced more pain. Risk factors for PJK included age at operation, low bone mineral density, shorter fusion constructs, upper instrumented vertebrae below L2, and inadequate restoration of global sagittal balance. CONCLUSIONS: Prevalence of PJK following long spinal fusion for adult spinal deformity was high but not clinically significant. Careful and detailed preoperative planning and surgical execution may reduce PJK in adult spinal deformity patients.

Innovative Developments in Lumbar Interbody Cage Materials and Design: A Comprehensive Narrative Review.

This review comprehensively examines the evolution and current state of interbody cage technology for lumbar interbody fusion (LIF). This review highlights the biomechanical and clinical implications of the transition from traditional static cage designs to advanced expandable variants for spinal surgery. The review begins by exploring the early developments in cage materials, highlighting the roles of titanium and polyetheretherketone in the advancement of LIF techniques. This review also discusses the strengths and limitations of these materials, leading to innovations in surface modifications and the introduction of novel materials, such as tantalum, as alternative materials. Advancements in three-dimensional printing and surface modification technologies form a significant part of this review, emphasizing the role of these technologies in enhancing the biomechanical compatibility and osseointegration of interbody cages. In addition, this review explores the increase in biodegradable and composite materials such as polylactic acid and polycaprolactone, addressing their potential to mitigate long-term implant-related complications. A critical evaluation of static and expandable cages is presented, including their respective clinical and radiological outcomes. While static cages have been a mainstay of LIF, expandable cages are noted for their adaptability to the patient's anatomy, reducing complications such as cage subsidence. However, this review highlights the ongoing debate and the lack of conclusive evidence regarding the superiority of either cage type in terms of clinical outcomes. Finally, this review proposes future directions for cage technology, focusing on the integration of bioactive substances and multifunctional coatings and the development of patient-specific implants. These advancements aim to further enhance the efficacy, safety, and personalized approach of spinal fusion surgeries. Moreover, this review offers a nuanced understanding of the evolving landscape of cage technology in LIF and provides insights into current practices and future possibilities in spinal surgery.

Perioperative pain protocols following surgery for adolescent idiopathic scoliosis: a snapshot of current treatments utilized by attending orthopedic surgeons.

PURPOSE: Perioperative management after adolescent idiopathic scoliosis (AIS) surgery varies extensively between surgeons and institutions. We devised a questionnaire to assess surgeon baseline characteristics, practice settings, and pain regimens to assess what factors contribute to perioperative pain protocols. METHODS: A multiple-choice questionnaire including 130 independent variables regarding baseline characteristics, practice environments, and pain regimen protocols was distributed to elicit information among surgeons performing AIS fusion surgery. Pairwise bivariate analysis between practice location, length of practice, and practice environment vs. type of post-operative analgesia was completed using two-tailed Fisher's exact test. RESULTS: 85 respondents participated, all identified as practicing orthopedic surgeons. The largest group of respondents reported 20-40% of their total practice was dedicated to AIS (36%). Respondents were predominantly hospital-employed academic physicians (67%). The most common pain medication administered preoperatively was gabapentin (54%). Postoperative regimens were highly varied. Discharge pain regimens most commonly included short-acting opiates (89%), acetaminophen (86%), antispasmodics (59%), and NSAIDs (51%). Bivariate analysis revealed that fentanyl PCA was significantly associated with practice location (p < 0.05). Utilization of NSAIDs was significantly associated with length in training, with older physicians utilizing anti-inflammatories more regularly than younger physicians (p < 0.05). CONCLUSION: This study identifies common perioperative regimens utilized in AIS surgery. Of interest, younger surgeons are less likely to prescribe NSAIDs post-operatively than surgeons who have been in practice for longer periods of time, which may represent a bias against anti-inflammatory medications in younger surgeons.

ChatGPT and its Role in the Decision-Making for the Diagnosis and Treatment of Lumbar Spinal Stenosis: A Comparative Analysis and Narrative Review.

STUDY DESIGN: Comparative Analysis and Narrative Review. OBJECTIVE: To assess and compare ChatGPT's responses to the clinical questions and recommendations proposed by The 2011 North American Spine Society (NASS) Clinical Guideline for the Diagnosis and Treatment of Degenerative Lumbar Spinal Stenosis (LSS). We explore the advantages and disadvantages of ChatGPT's responses through an updated literature review on spinal stenosis. METHODS: We prompted ChatGPT with questions from the NASS Evidence-based Clinical Guidelines for LSS and compared its generated responses with the recommendations provided by the guidelines. A review of the literature was performed via PubMed, OVID, and Cochrane on the diagnosis and treatment of lumbar spinal stenosis between January 2012 and April 2023. RESULTS: 14 questions proposed by the NASS guidelines for LSS were uploaded into ChatGPT and directly compared to the responses offered by NASS. Three questions were on the definition and history of LSS, one on diagnostic tests, seven on non-surgical interventions and three on surgical interventions. The review process found 40 articles that were selected for inclusion that helped corroborate or contradict the responses that were generated by ChatGPT. CONCLUSIONS: ChatGPT's responses were similar to findings in the current literature on LSS. These results demonstrate the potential for implementing ChatGPT into the spine surgeon's workplace as a means of supporting the decision-making process for LSS diagnosis and treatment. However, our narrative summary only provides a limited literature review and additional research is needed to standardize our findings as means of validating ChatGPT's use in the clinical space.

Can generative artificial intelligence pass the orthopaedic board examination?

Background: Resident training programs in the US use the Orthopaedic In-Training Examination (OITE) developed by the American Academy of Orthopaedic Surgeons (AAOS) to assess the current knowledge of their residents and to identify the residents at risk of failing the Amerian Board of Orthopaedic Surgery (ABOS) examination. Optimal strategies for OITE preparation are constantly being explored. There may be a role for Large Language Models (LLMs) in orthopaedic resident education. ChatGPT, an LLM launched in late 2022 has demonstrated the ability to produce accurate, detailed answers, potentially enabling it to aid in medical education and clinical decision-making. The purpose of this study is to evaluate the performance of ChatGPT on Orthopaedic In-Training Examinations using Self-Assessment Exams from the AAOS database and approved literature as a proxy for the Orthopaedic Board Examination. Methods: 301 SAE questions from the AAOS database and associated AAOS literature were input into ChatGPT's interface in a question and multiple-choice format and the answers were then analyzed to determine which answer choice was selected. A new chat was used for every question. All answers were recorded, categorized, and compared to the answer given by the OITE and SAE exams, noting whether the answer was right or wrong. Results: Of the 301 questions asked, ChatGPT was able to correctly answer 183 (60.8%) of them. The subjects with the highest percentage of correct questions were basic science (81%), oncology (72.7%, shoulder and elbow (71.9%), and sports (71.4%). The questions were further subdivided into 3 groups: those about management, diagnosis, or knowledge recall. There were 86 management questions and 47 were correct (54.7%), 45 diagnosis questions with 32 correct (71.7%), and 168 knowledge recall questions with 102 correct (60.7%). Conclusions: ChatGPT has the potential to provide orthopedic educators and trainees with accurate clinical conclusions for the majority of board-style questions, although its reasoning should be carefully analyzed for accuracy and clinical validity. As such, its usefulness in a clinical educational context is currently limited but rapidly evolving. Clinical relevance: ChatGPT can access a multitude of medical data and may help provide accurate answers to clinical questions.

An analysis of ChatGPT recommendations for the diagnosis and treatment of cervical radiculopathy.

OBJECTIVE: The objective of this study was to assess the safety and accuracy of ChatGPT recommendations in comparison to the evidence-based guidelines from the North American Spine Society (NASS) for the diagnosis and treatment of cervical radiculopathy. METHODS: ChatGPT was prompted with questions from the 2011 NASS clinical guidelines for cervical radiculopathy and evaluated for concordance. Selected key phrases within the NASS guidelines were identified. Completeness was measured as the number of overlapping key phrases between ChatGPT responses and NASS guidelines divided by the total number of key phrases. A senior spine surgeon evaluated the ChatGPT responses for safety and accuracy. ChatGPT responses were further evaluated on their readability, similarity, and consistency. Flesch Reading Ease scores and Flesch-Kincaid reading levels were measured to assess readability. The Jaccard Similarity Index was used to assess agreement between ChatGPT responses and NASS clinical guidelines. RESULTS: A total of 100 key phrases were identified across 14 NASS clinical guidelines. The mean completeness of ChatGPT-4 was 46%. ChatGPT-3.5 yielded a completeness of 34%. ChatGPT-4 outperformed ChatGPT-3.5 by a margin of 12%. ChatGPT-4.0 outputs had a mean Flesch reading score of 15.24, which is very difficult to read, requiring a college graduate education to understand. ChatGPT-3.5 outputs had a lower mean Flesch reading score of 8.73, indicating that they are even more difficult to read and require a professional education level to do so. However, both versions of ChatGPT were more accessible than NASS guidelines, which had a mean Flesch reading score of 4.58. Furthermore, with NASS guidelines as a reference, ChatGPT-3.5 registered a mean ± SD Jaccard Similarity Index score of 0.20 ± 0.078 while ChatGPT-4 had a mean of 0.18 ± 0.068. Based on physician evaluation, outputs from ChatGPT-3.5 and ChatGPT-4.0 were safe 100% of the time. Thirteen of 14 (92.8%) ChatGPT-3.5 responses and 14 of 14 (100%) ChatGPT-4.0 responses were in agreement with current best clinical practices for cervical radiculopathy according to a senior spine surgeon. CONCLUSIONS: ChatGPT models were able to provide safe and accurate but incomplete responses to NASS clinical guideline questions about cervical radiculopathy. Although the authors' results suggest that improvements are required before ChatGPT can be reliably deployed in a clinical setting, future versions of the LLM hold promise as an updated reference for guidelines on cervical radiculopathy. Future versions must prioritize accessibility and comprehensibility for a diverse audience.

Use of ChatGPT for Determining Clinical and Surgical Treatment of Lumbar Disc Herniation With Radiculopathy: A North American Spine Society Guideline Comparison.

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.

Association Between Age-stratified Cohorts and Perioperative Complications and 30-day and 90-day Readmission in Patients Undergoing Single-level Anterior Cervical Discectomy and Fusion.

STUDY DESIGN: Retrospective analysis. OBJECTIVE: To assess perioperative complication rates and readmission rates after ACDF in a patient population of advanced age. SUMMARY OF BACKGROUND DATA: Readmission rates after ACDF are important markers of surgical quality and, with recent shifts in reimbursement schedules, they are rapidly gaining weight in the determination of surgeon and hospital reimbursement. METHODS: Patients 18 years of age and older who underwent elective single-level ACDF were identified in the National Readmissions Database (NRD) and stratified into 4 cohorts: 18-39 ("young"), 40-64 ("middle"), 65-74 ("senior"), and 75+ ("elderly") years of age. For each cohort, the perioperative complications, frequency of those complications, and number of patients with at least 1 readmission within 30 and 90 days of discharge were analyzed. χ 2 tests were used to calculate likelihood of complications and readmissions. RESULTS: There were 1174 "elderly" patients in 2016, 1072 in 2017, and 1010 in 2018 who underwent ACDF. Their rate of any complication was 8.95%, 11.00%, and 13.47%, respectively ( P <0.0001), with dysphagia and acute posthemorrhagic anemia being the most common across all 3 years. They experienced complications at a greater frequency than their younger counterparts (15.80%, P <0.0001; 16.98%, P <0.0001; 21.68%, P <0.0001). They also required 30-day and 90-day readmission more frequently ( P <0.0001). CONCLUSION: It has been well-established that advanced patient age brings greater risk of perioperative complications in ACDF surgery. What remains unsettled is the characterization of this age-complication relationship within specific age cohorts and how these complications inform patient hospital course. Our study provides an updated analysis of age-specific complications and readmission rates in ACDF patients. Orthopedic surgeons may account for the rise in complication and readmission rates in this population with the corresponding reduction in length and stay and consider this relationship before discharging elderly ACDF patients.

Reliable Prediction of Discharge Disposition Following Cervical Spine Surgery With Ensemble Machine Learning and Validation on a National Cohort.

STUDY DESIGN: A retrospective cohort study. OBJECTIVE: The purpose of this study is to develop a machine learning algorithm to predict nonhome discharge after cervical spine surgery that is validated and usable on a national scale to ensure generalizability and elucidate candidate drivers for prediction. SUMMARY OF BACKGROUND DATA: Excessive length of hospital stay can be attributed to delays in postoperative referrals to intermediate care rehabilitation centers or skilled nursing facilities. Accurate preoperative prediction of patients who may require access to these resources can facilitate a more efficient referral and discharge process, thereby reducing hospital and patient costs in addition to minimizing the risk of hospital-acquired complications. METHODS: Electronic medical records were retrospectively reviewed from a single-center data warehouse (SCDW) to identify patients undergoing cervical spine surgeries between 2008 and 2019 for machine learning algorithm development and internal validation. The National Inpatient Sample (NIS) database was queried to identify cervical spine fusion surgeries between 2009 and 2017 for external validation of algorithm performance. Gradient-boosted trees were constructed to predict nonhome discharge across patient cohorts. The area under the receiver operating characteristic curve (AUROC) was used to measure model performance. SHAP values were used to identify nonlinear risk factors for nonhome discharge and to interpret algorithm predictions. RESULTS: A total of 3523 cases of cervical spine fusion surgeries were included from the SCDW data set, and 311,582 cases were isolated from NIS. The model demonstrated robust prediction of nonhome discharge across all cohorts, achieving an area under the receiver operating characteristic curve of 0.87 (SD=0.01) on both the SCDW and nationwide NIS test sets. Anterior approach only, age, elective admission status, Medicare insurance status, and total Elixhauser Comorbidity Index score were the most important predictors of discharge destination. CONCLUSIONS: Machine learning algorithms reliably predict nonhome discharge across single-center and national cohorts and identify preoperative features of importance following cervical spine fusion surgery.

Performance of ChatGPT on NASS Clinical Guidelines for the Diagnosis and Treatment of Low Back Pain: A Comparison Study.

STUDY DESIGN: Comparative analysis. OBJECTIVE: To evaluate Chat Generative Pre-trained Transformer (ChatGPT's) ability to predict appropriate clinical recommendations based on the most recent clinical guidelines for the diagnosis and treatment of low back pain. BACKGROUND: Low back pain is a very common and often debilitating condition that affects many people globally. ChatGPT is an artificial intelligence model that may be able to generate recommendations for low back pain. MATERIALS AND METHODS: Using the North American Spine Society Evidence-Based Clinical Guidelines as the gold standard, 82 clinical questions relating to low back pain were entered into ChatGPT (GPT-3.5) independently. For each question, we recorded ChatGPT's answer, then used a point-answer system-the point being the guideline recommendation and the answer being ChatGPT's response-and asked ChatGPT if the point was mentioned in the answer to assess for accuracy. This response accuracy was repeated with one caveat-a prior prompt is given in ChatGPT to answer as an experienced orthopedic surgeon-for each question by guideline category. A two-sample proportion z test was used to assess any differences between the preprompt and postprompt scenarios with alpha=0.05. RESULTS: ChatGPT's response was accurate 65% (72% postprompt, P =0.41) for guidelines with clinical recommendations, 46% (58% postprompt, P =0.11) for guidelines with insufficient or conflicting data, and 49% (16% postprompt, P =0.003*) for guidelines with no adequate study to address the clinical question. For guidelines with insufficient or conflicting data, 44% (25% postprompt, P =0.01*) of ChatGPT responses wrongly suggested that sufficient evidence existed. CONCLUSION: ChatGPT was able to produce a sufficient clinical guideline recommendation for low back pain, with overall improvements if initially prompted. However, it tended to wrongly suggest evidence and often failed to mention, especially postprompt, when there is not enough evidence to adequately give an accurate recommendation.

The Effect of Intraoperative Overdistraction on Subsidence Following Anterior Cervical Discectomy and Fusion.

STUDY DESIGN: Retrospective cohort. OBJECTIVE: The purpose of this study was to evaluate the effect of overdistraction on interbody cage subsidence. BACKGROUND: Vertebral overdistraction due to the use of large intervertebral cage sizes may increase the risk of postoperative subsidence. METHODS: Patients who underwent anterior cervical discectomy and fusion between 2016 and 2021 were included. All measurements were performed using lateral cervical radiographs at 3 time points - preoperative, immediate postoperative, and final follow-up >6 months postoperatively. Anterior and posterior distraction were calculated by subtracting the preoperative disc height from the immediate postoperative disc height. Cage subsidence was calculated by subtracting the final follow-up postoperative disc height from the immediate postoperative disc height. Associations between anterior and posterior subsidence and distraction were determined using multivariable linear regression models. The analyses controlled for cage type, cervical level, sex, age, smoking status, and osteopenia. RESULTS: Sixty-eight patients and 125 fused levels were included in the study. Of the 68 fusions, 22 were single-level fusions, 35 were 2-level, and 11 were 3-level. The median final follow-up interval was 368 days (range: 181-1257 d). Anterior disc space subsidence was positively associated with anterior distraction (beta = 0.23; 95% CI: 0.08, 0.38; P = 0.004), and posterior disc space subsidence was positively associated with posterior distraction (beta = 0.29; 95% CI: 0.13, 0.45; P < 0.001). No significant associations between anterior distraction and posterior subsidence (beta = 0.07; 95% CI: -0.06, 0.20; P = 0.270) or posterior distraction and anterior subsidence (beta = 0.06; 95% CI: -0.14, 0.27; P = 0.541) were observed. CONCLUSIONS: We found that overdistraction of the disc space was associated with increased postoperative subsidence after anterior cervical discectomy and fusion. Surgeons should consider choosing a smaller cage size to avoid overdistraction and minimize postoperative subsidence.

Performance of a Large Language Model in the Generation of Clinical Guidelines for Antibiotic Prophylaxis in Spine Surgery.

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.

Surgeon Preference Regarding Wound Dressing Management in Lumbar Fusion Surgery: An AO Spine Global Cross-Sectional Study.

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.