Efficacy and safety of avacincaptad pegol in patients with geographic atrophy (GATHER2): 12-month results from a randomised, double-masked, phase 3 trial.

BACKGROUND: Geographic atrophy is an advanced form of dry age-related macular degeneration that can lead to irreversible vision loss and high burden of disease. We aimed to assess efficacy and safety of avacincaptad pegol 2 mg in reducing geographic atrophy lesion growth. METHODS: GATHER2 is a randomised, double-masked, sham-controlled, 24-month, phase 3 trial across 205 retina clinics, research hospitals, and academic institutions globally. To be eligible, patients had to be aged 50 years or older with non-centrepoint-involving geographic atrophy and best corrected visual acuity between 20/25 and 20/320 in the study eye. Eligible patients were randomly assigned (1:1) to monthly avacincaptad pegol 2 mg administered as a 100 µL intravitreal injection or sham for the first 12 months. Randomisation was performed using an interactive response technology system with stratification by factors known to be of prognostic importance in age-related macular degeneration. Patients, investigators, study centre staff, sponsor personnel, and data analysts were masked to treatment allocation. The primary endpoint was geographic atrophy lesion size measured by fundus autofluorescence at baseline, month 6, and month 12. Efficacy and safety analyses were done in the modified intention-to-treat and safety populations, respectively. This trial is registered with ClinicalTrials.gov, NCT04435366. FINDINGS: Between June 22, 2020, and July 23, 2021, 1422 patients were screened for eligibility, of whom 448 were enrolled and randomly assigned to avacincaptad pegol 2 mg (n=225) or sham (n=223). One patient in the sham group did not receive study treatment and was excluded from analyses. There were 154 (68%) female patients and 71 (32%) male patients in the avacincaptad pegol 2 mg group, and 156 (70%) female patients and 66 (30%) male patients in the sham group. From baseline to month 12, the mean rate of square-root-transformed geographic atrophy area growth was 0·336 mm/year (SE 0·032) with avacincaptad pegol 2 mg and 0·392 mm/year (0·033) with sham, a difference in growth of 0·056 mm/year (95% CI 0·016-0·096; p=0·0064), representing a 14% difference between the avacincaptad pegol 2 mg group and the sham group. Ocular treatment-emergent adverse events in the study eye occurred in 110 (49%) patients in the avacincaptad pegol 2 mg group and 83 (37%) in the sham group. There were no endophthalmitis, intraocular inflammation, or ischaemic optic neuropathy events over 12 months. To month 12, macular neovascularisation in the study eye occurred in 15 (7%) patients in the avacincaptad pegol 2 mg group and nine (4%) in the sham group, with exudative macular neovascularisation occurring in 11 (5%) in the avacincaptad pegol 2 mg group and seven (3%) in the sham group. INTERPRETATION: Monthly avacincaptad pegol 2 mg was well tolerated and showed significantly slower geographic atrophy growth over 12 months than sham treatment, suggesting that avacincaptad pegol might slow disease progression and potentially change the trajectory of disease for patients with geographic atrophy. FUNDING: Iveric Bio, An Astellas Company.

Incident heart failure and the subsequent risk of progression to end stage kidney disease in individuals with type 2 diabetes.

BACKGROUND: Diabetic kidney disease is an established risk factor for heart failure. However, the impact of incident heart failure on the subsequent risk of renal failure has not been systematically assessed in diabetic population. We sought to study the risk of progression to end stage kidney disease (ESKD) after incident heart failure in Asian patients with type 2 diabetes. METHODS: In this prospective cohort study, 1985 outpatients with type 2 diabetes from a regional hospital and a primary care facility in Singapore were followed for a median of 8.6 (interquartile range 6.2-9.6) years. ESKD was defined as a composite of progression to sustained eGFR below 15 ml/min/1.73m2, maintenance dialysis or renal death, whichever occurred first. RESULTS: 180 incident heart failure events and 181 incident ESKD events were identified during follow-up. Of 181 ESKD events, 38 (21%) occurred after incident heart failure. Compared to those did not progress to ESKD after incident heart failure (n = 142), participants who progressed to ESKD after heart failure occurrence were younger, had higher HbA1c and higher urine albumin-to-creatinine ratio at baseline. The excess risk of ESKD manifested immediately after heart failure occurrence, persisted for two years and was moderated thereafter. Cox regression suggested that, compared to counterparts with no heart failure event, participants with heart failure occurrence had 9.6 (95% CI 5.0- 18.3) fold increased risk for incident ESKD after adjustment for baseline cardio-renal risk factors including eGFR and albuminuria. It appeared that heart failure with preserved ejection fraction had a higher risk for ESKD as compared to those with reduced ejection fraction (adjusted HR 13.7 [6.3-29.5] versus 6.5 [2.3-18.6]). CONCLUSION: Incident heart failure impinges a high risk for progression to ESKD in individuals with type 2 diabetes. Our data highlight the need for intensive surveillance of kidney function after incident heart failure, especially within the first two years after heart failure diagnosis.

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.

Sentiment Analysis of Online Patient-Written Reviews of Vascular Surgeons.

BACKGROUND: Online patient reviews influence a patient's choice of a vascular surgeon. The aim of this study is to examine underlying factors that contribute to positive and negative patient reviews by leveraging sentiment analysis and machine learning methods. METHODS: The Society of Vascular Surgeons publicly accessible member directory was queried and cross-referenced with a popular patient-maintained physician review website, healthgrades.com. Sentiment analysis and machine learning methods were used to analyze several parameters. Demographics (gender, age, and state of practice), star rating (of 5 stars), and written reviews were obtained for corresponding vascular surgeons. A sentiment analysis model was applied to patient-written reviews and validated against the star ratings. Student's t-test or one-way analysis of variance assessed demographic relationships with reviews. Word frequency assessments and multivariable logistic regression analyses were conducted to identify common and determinative components of written reviews. RESULTS: A total of 1,799 vascular surgeons had public profiles with reviews. Female gender of surgeon was associated with lower star ratings (male = 4.19, female = 3.95, P < 0.01) and average sentiment score (male = 0.50, female = 0.40, P < 0.01). Younger physician age was associated with higher star rating (P = 0.02) but not average sentiment score (P = 0.12). In the Best reviews, the most commonly used one-words were Care (N = 999), Caring (N = 767), and Kind (N = 479), while the most commonly used two-word pairs were Saved/Life (N = 189), Feel/Comfortable (N = 106), and Kind/Caring (N = 104). For the Worst reviews, the most commonly used one-words were Pain (N = 254) and Rude (N = 148), while the most commonly used two-word pairs were No/One (N = 27), Waste/Time (N = 25), and Severe/Pain (N = 18). In a multiple logistic regression, satisfactory reviews were associated with words such as Confident (odds ratio [OR] = 8.93), Pain-free (OR = 4.72), Listens (OR = 2.55), and Bedside Manner (OR = 1.70), while unsatisfactory reviews were associated with words such as Rude (OR = 0.01), Arrogant (OR = 0.09), Infection (OR = 0.20), and Wait (OR = 0.48). CONCLUSIONS: Female surgeons received significantly worse reviews and younger surgeons tended to receive better reviews. The positivity and negativity of reviews were largely related to words associated with the patient-doctor experience and pain. Vascular surgeons should focus on these 2 areas to improve patient experiences and their own reviews.

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.

How Are We Doing? A Scoping Review of Published Patient-Centered Outcomes Research in United States Student-Run Free Clinics.

Phenomenon: Student-run free clinics (SRFCs) serve an integral role in most United States (US) medical schools and contribute substantially to literature on the quality of care to uninsured persons. There has been substantial growth over the past decade of scholarly work produced by SRFCs as they have increased in size and number. Research on patient care outcomes informs better care structures for patients, however there is no current synthesis of patient care outcomes research among SRFCs. This article provides an overview of SRFC research on patient outcomes to understand current research domains and to identify gaps in the literature. Approach: We completed a scoping review by searching Scopus, PubMed, and Journal of Student Run Clinics in June 2021. All peer-reviewed, English-language articles focused on patient-centered outcomes at SRFCs in the US were included. Two independent reviewers performed title, abstract, and full-text screening of relevant works, and eight reviewers conducted data extraction. Descriptive data analysis was performed along with relevant content analysis of patient-centered outcomes. Findings: The search strategy identified 784 studies, of which 87 met inclusion criteria. Most studies were published within the last six years (81.6%), located in California, New York, or Florida (43.7%), and intervention based (33.3%). Many studies (46.0%) had a specific disease of focus of which diabetes was the most researched(19.5%). Patient-centered studies were the leading focus of the study aims (40.2%), where key findings demonstrated primarily improved outcomes in clinic metrics post-intervention (36.8%) or equivalent/better clinical performance than national metrics (20.7%). Insights: This review brings to light gaps in the literature reporting research in SRFCs and can be applied to other low-resource settings. Future efforts to expand SRFC outcomes research should focus on community relationship building, understanding institutional support, and ensuring education on best practices for research within SRFCs. Doing so informs patient care improvement as SRFCs continue to operate as safety net clinics for marginalized populations.

Reducing PTV margins for prostate SBRT with motion compensation and gating techniques.

The purpose of this study is to investigate the dosimetric accuracy of prostate SBRT when motion is considered. To account for target movement, motion compensation and gating techniques were investigated with PTV margins reduced to 2 mm. To allow for dosimetric measurements a Delta4 phantom, Gafchromic film, and Hexamotion motion platform were utilized. Four motion files were utilized that represent a range of motions. Analysis of measured prostate motions for fifteen patients was performed to ensure detected motions were similar to those previously reported and motion files utilized were suitable. Five patient plans were utilized to allow for the effects of MLC and target motion interplay to be investigated. For both motion compensation and gating techniques, plans were delivered to the stationary phantom and for each of four motion types with/without compensation/gating enabled. Using a 3%, 2 mm and 80% threshold gamma criteria, film measurements had an average pass rate of 80.5% for uncorrected deliveries versus 96.0% for motion compensated deliveries. For gated techniques average pass rates increased from 89.9% for uncorrected to 94.8% with gating enabled. Measurements with the Delta4 arrays were analyzed with a 3%, 2 mm and 10% threshold dose. An average pass rate of 83.8% was measured for uncorrected motions versus 94.8% with motion compensation. For the gated technique an average pass rate of 87.2% was found for uncorrected motions versus 96.9% with gating enabled. These results show that very high gamma pass rates are achievable when motion compensation or gating techniques are applied. When target motion is not accounted for shifts up to 5 mm in planned versus delivered isodose distributions were found. However, when motion compensation, or gated techniques were applied, much smaller differences between planned and delivered isodose distributions were found. With these techniques dose delivery accuracy is greatly improved, allowing for PTV margins to be reduced.

Clinical variable-based cluster analysis identifies novel subgroups with a distinct genetic signature, lipidomic pattern and cardio-renal risks in Asian patients with recent-onset type 2 diabetes.

AIMS/HYPOTHESIS: We sought to subtype South East Asian patients with type 2 diabetes by de novo cluster analysis on clinical variables, and to determine whether the novel subgroups carry distinct genetic and lipidomic features as well as differential cardio-renal risks. METHODS: Analysis by k-means algorithm was performed in 687 participants with recent-onset diabetes in Singapore. Genetic risk for beta cell dysfunction was assessed by polygenic risk score. We used a discovery-validation approach for the lipidomics study. Risks for cardio-renal complications were studied by survival analysis. RESULTS: Cluster analysis identified three novel diabetic subgroups, i.e. mild obesity-related diabetes (MOD, 45%), mild age-related diabetes with insulin insufficiency (MARD-II, 36%) and severe insulin-resistant diabetes with relative insulin insufficiency (SIRD-RII, 19%). Compared with the MOD subgroup, MARD-II had a higher polygenic risk score for beta cell dysfunction. The SIRD-RII subgroup had higher levels of sphingolipids (ceramides and sphingomyelins) and glycerophospholipids (phosphatidylethanolamine and phosphatidylcholine), whereas the MARD-II subgroup had lower levels of sphingolipids and glycerophospholipids but higher levels of lysophosphatidylcholines. Over a median of 7.3 years follow-up, the SIRD-RII subgroup had the highest risks for incident heart failure and progressive kidney disease, while the MARD-II subgroup had moderately elevated risk for kidney disease progression. CONCLUSIONS/INTERPRETATION: Cluster analysis on clinical variables identified novel subgroups with distinct genetic, lipidomic signatures and varying cardio-renal risks in South East Asian participants with type 2 diabetes. Our study suggests that this easily actionable approach may be adapted in other ethnic populations to stratify the heterogeneous type 2 diabetes population for precision medicine.

Pannexin 3 deletion reduces fat accumulation and inflammation in a sex-specific manner.

BACKGROUND: Pannexin 3 (PANX3) is a channel-forming glycoprotein that enables nutrient-induced inflammation in vitro, and genetic linkage data suggest that it regulates body mass index. Here, we characterized inflammatory and metabolic parameters in global Panx3 knockout (KO) mice in the context of forced treadmill running (FEX) and high-fat diet (HFD). METHODS: C57BL/6N (WT) and KO mice were randomized to either a FEX running protocol or no running (SED) from 24 until 30 weeks of age. Body weight was measured biweekly, and body composition was measured at 24 and 30 weeks of age. Male WT and KO mice were fed a HFD from 12 to 28 weeks of age. Metabolic organs were analyzed for a panel of inflammatory markers and PANX3 expression. RESULTS: In females there were no significant differences in body composition between genotypes, which could be due to the lack of PANX3 expression in female white adipose tissue, while male KOs fed a chow diet had lower body weight and lower fat mass at 24 and 30 weeks of age, which was reduced to the same extent as 6 weeks of FEX in WT mice. In addition, male KO mice exhibited significantly lower expression of multiple pro-inflammatory genes in white adipose tissue compared to WT mice. While on a HFD body weight differences were insignificant, multiple inflammatory genes were significantly different in quadriceps muscle and white adipose tissue resulting in a more anti-inflammatory phenotype in KO mice compared to WT. The lower fat mass in male KO mice may be due to significantly fewer adipocytes in their subcutaneous fat compared to WT mice. Mechanistically, adipose stromal cells (ASCs) cultured from KO mice grow significantly slower than WT ASCs. CONCLUSION: PANX3 is expressed in male adult mouse adipose tissue and may regulate adipocyte numbers, influencing fat accumulation and inflammation.

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.

The potential role of precision medicine to alleviate racial disparities in prostate, bladder and renal urological cancer care.

Background: Racial disparities in oncological outcomes resulting from differences in social determinants of health (SDOH) and tumour biology are well described in prostate cancer (PCa) but similar inequities exist in bladder (BCa) and renal cancers (RCCs). Precision medicine (PM) aims to provide personalized treatment based on individual patient characteristics and has the potential to reduce these inequities in GU cancers. Objective: This article aims to review the current evidence outlining racial disparities in GU cancers and explore studies demonstrating improved oncological outcomes when PM is applied to racially diverse patient populations. Evidence acquisition: Evidence was obtained from Pubmed and Web of Science using keywords prostate, bladder and renal cancer, racial disparity and precision medicine. Because limited studies were found, preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines were not applied but rather related articles were studied to explore existing debates, identify the current status and speculate on future applications. Results: Evidence suggests addressing SDOH for PCa can reverse racial inequities in oncological outcomes but differences in incidence remain. Similar disparities in BCa and RCC are seen, and it would be reasonable to suggest achieving parity in SDOH for all races would do the same. Research applying a PM approach to different ethnicities is lacking although in African Americans (AAs) with metastatic castrate-resistant prostate cancer (mCRPCa) better outcomes have been shown with androgen receptor inhibitors, radium-223 and sipuleucel. Exploiting the abscopal effect with targeted radiation therapy (RT) and immunotherapy has promise but requires further study, as does defining actionable mutations in specific patient groups to tailor treatments as appropriate. Conclusion: For all GU cancers, the historical underrepresentation of ethnic minorities in clinical trials still exists and there is an urgent need for recruitment strategies to address this. PM is a promising development with the potential to reduce inequities in GU cancers, however, both improved understanding of race-specific tumour biology, and enhanced recruitment of minority populations into clinical trials are required. Without this, the benefits of PM will be limited.

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.

Combining theranostic/particle therapy with immunotherapy for the treatment of GU malignancies.

Particle therapy and radiopharmaceuticals are emerging fields in the treatment of genitourinary cancers. With these novel techniques and the ever-growing immunotherapy options, the combinations of these therapies have the potential to improve current cancer cure rates. However, the most effective sequence and combination of these therapies is unknown and is a question that is actively being explored in multiple ongoing clinical trials. Here, we review the immunological effects of particle therapy and the available radiopharmaceuticals and discuss how best to combine these therapies.

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.

Evaluation of multiple-vendor AI autocontouring solutions.

BACKGROUND: Multiple artificial intelligence (AI)-based autocontouring solutions have become available, each promising high accuracy and time savings compared with manual contouring. Before implementing AI-driven autocontouring into clinical practice, three commercially available CT-based solutions were evaluated. MATERIALS AND METHODS: The following solutions were evaluated in this work: MIM-ProtégéAI+ (MIM), Radformation-AutoContour (RAD), and Siemens-DirectORGANS (SIE). Sixteen organs were identified that could be contoured by all solutions. For each organ, ten patients that had manually generated contours approved by the treating physician (AP) were identified, totaling forty-seven different patients. CT scans in the supine position were acquired using a Siemens-SOMATOMgo 64-slice helical scanner and used to generate autocontours. Physician scoring of contour accuracy was performed by at least three physicians using a five-point Likert scale. Dice similarity coefficient (DSC), Hausdorff distance (HD) and mean distance to agreement (MDA) were calculated comparing AI contours to "ground truth" AP contours. RESULTS: The average physician score ranged from 1.00, indicating that all physicians reviewed the contour as clinically acceptable with no modifications necessary, to 3.70, indicating changes are required and that the time taken to modify the structures would likely take as long or longer than manually generating the contour. When averaged across all sixteen structures, the AP contours had a physician score of 2.02, MIM 2.07, RAD 1.96 and SIE 1.99. DSC ranged from 0.37 to 0.98, with 41/48 (85.4%) contours having an average DSC ≥ 0.7. Average HD ranged from 2.9 to 43.3 mm. Average MDA ranged from 0.6 to 26.1 mm. CONCLUSIONS: The results of our comparison demonstrate that each vendor's AI contouring solution exhibited capabilities similar to those of manual contouring. There were a small number of cases where unusual anatomy led to poor scores with one or more of the solutions. The consistency and comparable performance of all three vendors' solutions suggest that radiation oncology centers can confidently choose any of the evaluated solutions based on individual preferences, resource availability, and compatibility with their existing clinical workflows. Although AI-based contouring may result in high-quality contours for the majority of patients, a minority of patients require manual contouring and more in-depth physician review.

Pannexin 3 deletion in mice results in knee osteoarthritis and intervertebral disc degeneration after forced treadmill running.

Pannexin 3 (Panx3) is a glycoprotein that forms mechanosensitive channels expressed in chondrocytes and annulus fibrosus cells of the intervertebral disc (IVD). Evidence suggests Panx3 plays contrasting roles in traumatic versus aging osteoarthritis (OA) and intervertebral disc degeneration (IDD). However, whether its deletion influences the response of joint tissue to forced use is unknown. The purpose of this study was to determine if Panx3 deletion in mice causes increased knee joint OA and IDD after forced treadmill running. Male and female wildtype (WT) and Panx3 knockout (KO) mice were randomized to either a no-exercise group (sedentary; SED) or daily forced treadmill running (forced exercise; FEX) from 24 to 30 weeks of age. Knee cartilage and IVD histopathology were evaluated by histology, while tibial secondary ossification centers were analyzed using microcomputed tomography (µCT). Both male and female Panx3 KO mice developed larger superficial defects of the tibial cartilage after forced treadmill running compared with SED WT mice. Additionally, Panx3 KO mice developed reduced bone volume, and female PANX3 KO mice had lengthening of the lateral tubercle at the intercondylar eminence. In the lower lumbar spine, both male and female Panx3 KO mice developed histopathological features of IDD after running compared to SED WT mice. These findings suggest that the combination of deleting Panx3 and forced treadmill running induces OA and causes histopathological changes associated with the degeneration of the IVDs in mice.

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.

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.

Vision Loss Reduction with Avacincaptad Pegol for Geographic Atrophy: A 12-Month Post Hoc Analysis of the GATHER1 and GATHER2 Trials.

PURPOSE: To evaluate the impact of reduction in geographic atrophy (GA) lesion growth on visual acuity in the GATHER trials using categorical outcome measures. DESIGN: Randomized, double-masked, sham-controlled phase 3 trials. PARTICIPANTS: Aged ≥50 years with noncenter point-involving GA and best-corrected visual acuity (BCVA) of 25 to 80 ETDRS letters in the study eye. METHODS: GATHER1 consisted of 2 parts. In part 1, 77 patients were randomized 1:1:1 to avacincaptad pegol (ACP) 1 mg, ACP 2 mg, and sham. In part 2, 209 patients were randomized 1:2:2 to ACP 2 mg, ACP 4 mg, and sham. In GATHER2, patients were randomized 1:1 to ACP 2 mg (n = 225) and sham (n = 223). A post hoc analysis of 12-month data for pooled ACP 2 mg and sham groups is reported. MAIN OUTCOME MEASURES: Proportion of study eyes that experienced ≥10-, ≥15-, or ≥20-BCVA ETDRS letter loss from baseline to month 12; time-to-event analysis of persistent vision loss of ≥10, ≥15, or≥ 20 BCVA letters from baseline at ≥2 consecutive visits over 12 months; proportion of study eyes with BCVA loss to a level below driving eligibility threshold at month 12 among those eligible to drive at baseline. RESULTS: Lower proportions of study eyes experienced ≥10-, ≥15-, or ≥20-BCVA letter loss from baseline over 12 months with ACP 2 mg (11.6%, 4.0%, and 1.6%, respectively) versus sham (14.1%, 7.6%, and 4.5%, respectively). There was a reduction in the risk of persistent loss of ≥15 BCVA ETDRS letters with ACP 2 mg (3.4%) versus sham (7.8%) through 12 months. A lower proportion of study eyes treated with ACP 2 mg reached the threshold for driving ineligibility versus sham by 12 months. CONCLUSIONS: Treatment with ACP 2 mg delayed the risk of progression to persistent vision loss (i.e., ≥10-, ≥15-, and ≥20-BCVA letter loss or BCVA loss to a level below driving eligibility threshold) versus sham over 12 months. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.