ABSTRACT
STUDY DESIGN: Retrospective cohort study. OBJECTIVES: This study assessed the effectiveness of a popular large language model, ChatGPT-4, in predicting Current Procedural Terminology (CPT) codes from surgical operative notes. By employing a combination of prompt engineering, natural language processing (NLP), and machine learning techniques on standard operative notes, the study sought to enhance billing efficiency, optimize revenue collection, and reduce coding errors. METHODS: The model was given 3 different types of prompts for 50 surgical operative notes from 2 spine surgeons. The first trial was simply asking the model to generate CPT codes for a given OP note. The second trial included 3 OP notes and associated CPT codes to, and the third trial included a list of every possible CPT code in the dataset to prime the model. CPT codes generated by the model were compared to those generated by the billing department. Model evaluation was performed in the form of calculating the area under the ROC (AUROC), and area under precision-recall curves (AUPRC). RESULTS: The trial that involved priming ChatGPT with a list of every possible CPT code performed the best, with an AUROC of .87 and an AUPRC of .67, and an AUROC of .81 and AUPRC of .76 when examining only the most common CPT codes. CONCLUSIONS: ChatGPT-4 can aid in automating CPT billing from orthopedic surgery operative notes, driving down healthcare expenditures and enhancing billing code precision as the model evolves and fine-tuning becomes available.
ABSTRACT
Aneuploidy, the presence of chromosome gains or losses, is a hallmark of cancer. Here, we describe KaryoCreate (karyotype CRISPR-engineered aneuploidy technology), a system that enables the generation of chromosome-specific aneuploidies by co-expression of an sgRNA targeting chromosome-specific CENPA-binding É-satellite repeats together with dCas9 fused to mutant KNL1. We design unique and highly specific sgRNAs for 19 of the 24 chromosomes. Expression of these constructs leads to missegregation and induction of gains or losses of the targeted chromosome in cellular progeny, with an average efficiency of 8% for gains and 12% for losses (up to 20%) validated across 10 chromosomes. Using KaryoCreate in colon epithelial cells, we show that chromosome 18q loss, frequent in gastrointestinal cancers, promotes resistance to TGF-ß, likely due to synergistic hemizygous deletion of multiple genes. Altogether, we describe an innovative technology to create and study chromosome missegregation and aneuploidy in the context of cancer and beyond.
