BACKGROUND: High level evidence for direct oral anticoagulants (DOACs) in patients with cerebral venous thrombosis is lacking. We performed a systematic review and meta-analysis to assess the efficacy and safety of DOACs versus vitamin K antagonists in patients with cerebral venous thrombosis. METHODS: This systematic review was registered in PROSPERO (CRD42021228800). We searched MEDLINE (via Ovid), EMBASE, CINAHL, and the Web of Science Core Collection between January 1, 2007 and Feb 22, 2022. Search terms included a combination of keywords and controlled vocabulary terms for cerebral venous thrombosis, vitamin K antagonists/warfarin, and DOACs. We included both randomized and nonrandomized studies that compared vitamin K antagonists and DOACs in 5 or more patients with cerebral venous thrombosis. Where studies were sufficiently similar, we performed meta-analyses for efficacy (recurrent venous thromboembolism and complete recanalization) and safety (major hemorrhage) outcomes, using relative risks (RRs). RESULTS: Out of 10 665 records identified, we screened 254 as potentially eligible. Nineteen studies (16 observational studies [n=1735] and 3 randomized controlled trials [n=215]) met the inclusion criteria. All 3 randomized controlled trials had some concerns, and all 16 observational studies had at least moderate risk of bias. When compared with vitamin K antagonist treatment, DOAC had comparable risks of recurrent venous thromboembolism (relative risk [RR], 0.85 [95% CI, 0.52–1.37], I 2 =0%), major hemorrhage (RR, 0.70 [95% CI, 0.40–1.21], I 2 =0%), intracranial hemorrhage (RR, 0.58 [95% CI, 0.30–1.12]; I 2 =0%), death (RR, 1.14 [95% CI, 0.54–2.43], I 2 =1%), and complete venous recanalization (RR, 0.98 [95% CI, 0.87–1.11]; I 2 =0%). CONCLUSIONS: This systematic review and meta-analysis suggest that in patients with cerebral venous thrombosis, DOACs, and warfarin may have comparable efficacy and safety. Given the limitations of the studies included (low number of randomized controlled trials, modest total sample size, rare outcome events), our findings should be interpreted with caution pending confirmation by ongoing randomized controlled trials and large, prospective, observational studies.

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.

Objective: Subsidence following anterior cervical discectomy and fusion (ACDF) may lead to disruptions of cervical alignment and lordosis. The purpose of this study was to evaluate the effect of subsidence on segmental, regional, and global lordosis.Methods: This was a retrospective cohort study performed between 2016–2021 at a single institution. All measurements were performed using lateral cervical radiographs at the immediate postoperative period and at final follow-up greater than 6 months after surgery. Associations between subsidence and segmental lordosis, total fused lordosis, C2–7 lordosis, and cervical sagittal vertical alignment change were determined using Pearson correlation and multivariate logistic regression analyses.Results: One hundred thirty-one patients and 244 levels were included in the study. There were 41 one-level fusions, 67 two-level fusions, and 23 three-level fusions. The median follow-up time was 366 days (interquartile range, 239–566 days). Segmental subsidence was significantly negatively associated with segmental lordosis change in the Pearson (r = -0.154, p = 0.016) and multivariate analyses (beta = -3.78; 95% confidence interval, -7.15 to -0.42; p = 0.028) but no associations between segmental or total fused subsidence and any other measures of cervical alignment were observed.Conclusion: We found that subsidence is associated with segmental lordosis loss 6 months following ACDF. Surgeons should minimize subsidence to prevent long-term clinical symptoms associated with poor cervical alignment.

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Study Design Retrospective cohort. Objective Billing and coding-related administrative tasks are a major source of healthcare expenditure in the United States. We aim to show that a second-iteration Natural Language Processing (NLP) machine learning algorithm, XLNet, can automate the generation of CPT codes from operative notes in ACDF, PCDF, and CDA procedures. Methods We collected 922 operative notes from patients who underwent ACDF, PCDF, or CDA from 2015 to 2020 and included CPT codes generated by the billing code department. We trained XLNet, a generalized autoregressive pretraining method, on this dataset and tested its performance by calculating AUROC and AUPRC. Results The performance of the model approached human accuracy. Trial 1 (ACDF) achieved an AUROC of .82 (range: .48-.93), an AUPRC of .81 (range: .45-.97), and class-by-class accuracy of 77% (range: 34%-91%); trial 2 (PCDF) achieved an AUROC of .83 (.44-.94), an AUPRC of .70 (.45-.96), and class-by-class accuracy of 71% (42%-93%); trial 3 (ACDF and CDA) achieved an AUROC of .95 (.68-.99), an AUPRC of .91 (.56-.98), and class-by-class accuracy of 87% (63%-99%); trial 4 (ACDF, PCDF, CDA) achieved an AUROC of .95 (.76-.99), an AUPRC of .84 (.49-.99), and class-by-class accuracy of 88% (70%-99%). Conclusions We show that the XLNet model can be successfully applied to orthopedic surgeon’s operative notes to generate CPT billing codes. As NLP models as a whole continue to improve, billing can be greatly augmented with artificial intelligence assisted generation of CPT billing codes which will help minimize error and promote standardization in the process.

Miniaturized electrical stimulation (ES) implants show great promise in practice, but their real-time control by means of biophysical mechanistic algorithms is not feasible due to computational complexity. Here, we hypothesize machine learning methods can approximate these models while being much more computationally efficient. To prove this hypothesis, we estimate the normalized twitch force of the stimulated extensor digitorum longus muscle on n=11 Wistar rats with intra- and cross-subject calibration. After 2000 training stimulations, we reach a mean absolute error of 3% in an intra-subject setting and 20% in a cross-subject setting with a random forest regressor. To the best of our knowledge, this work is the first experiment showing the feasibility of AI to simulate complex ES mechanistic models. However, the results of cross-subject training motivate more research on error reduction methods for this setting.

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