A unique modular implant system enhances load sharing in anterior cervical interbody fusion: a finite element study

BACKGROUND: The efficacy of dynamic anterior cervical plates is somewhat controversial. Screws in static-plate designs have a smaller diameter and can cut through bone under load. While not ideal, this unintended loosening can help mitigate stress shielding. Stand-alone interbody devices with integral fixation have large endplate contact areas that may inhibit or prevent loosening of the fixation. This study investigates the load sharing ability of a novel dynamic plate design in preventing the stress shielding of the graft material compared to the non-dynamic devices. METHODS: An experimentally validated intact C5-C6 finite element model was modified to simulate discectomy and accommodate implant-graft assembly. Four implant iterations were modeled; InterPlate titanium device with dynamic surface features (springs), InterPlate titanium non-dynamic device, InterPlate titanium design having a fully enclosed graft chamber, and the InterPlate design in unfilled PEEK having a fully enclosed graft chamber. All the models were fixed at the inferior-most surface of C6 and the axial displacement required to completely embed the dynamic surface features was applied to the model. RESULTS: InterPlate device with dynamic surface features induced higher graft stresses compared to the other design iterations resulting in uniform load sharing. The distribution of these graft stresses were more uniform for the InterPlate dynamic design. CONCLUSIONS: These results indicate that the dynamic design decreases the stress shielding by increasing and more uniformly distributing the graft stress. Fully enclosed graft chambers increase stress shielding. Lower implant material modulus of elasticity does not reduce stress shielding significantly

Towards Conversational Diagnostic AI

At the heart of medicine lies the physician-patient dialogue, where skillful history-taking paves the way for accurate diagnosis, effective management, and enduring trust. Artificial Intelligence (AI) systems capable of diagnostic dialogue could increase accessibility, consistency, and quality of care. However, approximating clinicians' expertise is an outstanding grand challenge. Here, we introduce AMIE (Articulate Medical Intelligence Explorer), a Large Language Model (LLM) based AI system optimized for diagnostic dialogue. AMIE uses a novel self-play based simulated environment with automated feedback mechanisms for scaling learning across diverse disease conditions, specialties, and contexts. We designed a framework for evaluating clinically-meaningful axes of performance including history-taking, diagnostic accuracy, management reasoning, communication skills, and empathy. We compared AMIE's performance to that of primary care physicians (PCPs) in a randomized, double-blind crossover study of text-based consultations with validated patient actors in the style of an Objective Structured Clinical Examination (OSCE). The study included 149 case scenarios from clinical providers in Canada, the UK, and India, 20 PCPs for comparison with AMIE, and evaluations by specialist physicians and patient actors. AMIE demonstrated greater diagnostic accuracy and superior performance on 28 of 32 axes according to specialist physicians and 24 of 26 axes according to patient actors. Our research has several limitations and should be interpreted with appropriate caution. Clinicians were limited to unfamiliar synchronous text-chat which permits large-scale LLM-patient interactions but is not representative of usual clinical practice. While further research is required before AMIE could be translated to real-world settings, the results represent a milestone towards conversational diagnostic AI.Comment: 46 pages, 5 figures in main text, 19 figures in appendi

Towards Accurate Differential Diagnosis with Large Language Models

An accurate differential diagnosis (DDx) is a cornerstone of medical care, often reached through an iterative process of interpretation that combines clinical history, physical examination, investigations and procedures. Interactive interfaces powered by Large Language Models (LLMs) present new opportunities to both assist and automate aspects of this process. In this study, we introduce an LLM optimized for diagnostic reasoning, and evaluate its ability to generate a DDx alone or as an aid to clinicians. 20 clinicians evaluated 302 challenging, real-world medical cases sourced from the New England Journal of Medicine (NEJM) case reports. Each case report was read by two clinicians, who were randomized to one of two assistive conditions: either assistance from search engines and standard medical resources, or LLM assistance in addition to these tools. All clinicians provided a baseline, unassisted DDx prior to using the respective assistive tools. Our LLM for DDx exhibited standalone performance that exceeded that of unassisted clinicians (top-10 accuracy 59.1% vs 33.6%, [p = 0.04]). Comparing the two assisted study arms, the DDx quality score was higher for clinicians assisted by our LLM (top-10 accuracy 51.7%) compared to clinicians without its assistance (36.1%) (McNemar's Test: 45.7, p < 0.01) and clinicians with search (44.4%) (4.75, p = 0.03). Further, clinicians assisted by our LLM arrived at more comprehensive differential lists than those without its assistance. Our study suggests that our LLM for DDx has potential to improve clinicians' diagnostic reasoning and accuracy in challenging cases, meriting further real-world evaluation for its ability to empower physicians and widen patients' access to specialist-level expertise

Concurrent administration of heparin and activated protein C in a patient with pulmonary embolism and severe sepsis with positive outcome

Results of the PROWESS trial suggested that heparin may reduce the efficacy of recombinant human activated protein C (rhAPC) and the XPRESS study also showed increased bleeding complications in patients receiving heparin with rhAPC. Although it has been shown that heparin prophylaxis may be used along with rhAPC, no study has shown the interaction between continuous heparin infusion and rhAPC. Here, we report a case of severe sepsis with pulmonary embolism who was concurrently administered heparin and rhAPC infusions, with positive results and no bleeding complications

Towards a validated patient-specific computational modeling framework to identify failure regions in traditional growing rods in patients with early onset scoliosis

Background: While growing rods are an important contribution to early-onset scoliosis treatment, rod fractures are a common complication that require reoperations. A recent retrieval analysis study performed on failed traditional growing rods revealed that there are commonalities among patient characteristics based on the location of rod fracture. However, it remains unknown if these locations correspond to high stress regions in the implanted construct. Methods: A patient-specific finite element scoliotic model was developed to match the pre-operative (pre-op) scoliotic curve of a patient as described in previously published articles, and by using the patient registry information along with biplanar radiographs. A dual stainless-steel traditional growing rod construct was implanted into this scoliotic model and the surgical procedure was simulated to match the post-operative (post-op) scoliotic curve parameters. Muscle stabilization and gravity was simulated through follower load application. Rod distraction magnitudes were chosen based on pre-op to post-op cobb angle correction, and flexion bending load was simulated to identify the high stress regions on the rods. Results: The patient-specific finite element model identified two high stress regions on the posterior surface of the rods, one at mid construct and the other adjacent to the distal anchors. This correlated well with the data obtained from the retrieval analysis performed by researchers at U.S. Food and Drug Administration (FDA) which showed the posterior surface of the rod as the fracture initiation site, and the three locations of failure as mid-construct, adjacent to distal anchors, and adjacent to tandem connector. Conclusions: The result of this study confirms that the high stress regions on the growing rods, as identified by the FEA, match the fracture prone sites identified in the retrieval analysis performed at the FDA. This proof-of-concept patient-specific approach can be used to predict sites prone to fracture in growing rods