An Algorithmic Roadmap for the Surgical Management of Degenerative Cervical Myelopathy: A Narrative Review.

Degenerative cervical myelopathy (DCM) is a leading cause of disability, and its surgical management is crucial for improving patient neurological outcomes. Given the varied presentations and severities of DCM, treatment options are diverse. Surgeons often face challenges in selecting the most appropriate surgical approach because there is no universally correct answer. This narrative review aimed to aid the decision-making process in treating DCM by presenting a structured treatment algorithm. The authors categorized surgical scenarios based on an algorithm, outlining suitable treatment methods for each case. Four primary scenarios were identified based on the number of levels requiring surgery and K-line status: (1) K-line (+) and ≤3 levels, (2) K-line (+) and ≥3 levels, (3) K-line (-) and ≤3 levels, and (4) K-line (-) and ≥3 levels. This categorization aids in determining the appropriateness of anterior or posterior approaches and the necessity for fusion, considering the surgical level and K-line status. The complexity of surgical situations and diversity of treatment methods for DCM can be effectively managed using an algorithmic approach. Furthermore, surgical techniques that minimize the stages and address challenging conditions could enhance treatment outcomes in DCM.

Can Natural Language Processing and Artificial Intelligence Automate The Generation of Billing Codes From Operative Note Dictations?

STUDY DESIGN: Retrospective Cohort Study. OBJECTIVES: Using natural language processing (NLP) in combination with machine learning on standard operative notes may allow for efficient billing, maximization of collections, and minimization of coder error. This study was conducted as a pilot study to determine if a machine learning algorithm can accurately identify billing Current Procedural Terminology (CPT) codes on patient operative notes. METHODS: This was a retrospective analysis of operative notes from patients who underwent elective spine surgery by a single senior surgeon from 9/2015 to 1/2020. Algorithm performance was measured by performing receiver operating characteristic (ROC) analysis, calculating the area under the ROC curve (AUC) and the area under the precision-recall curve (AUPRC). A deep learning NLP algorithm and a Random Forest algorithm were both trained and tested on operative notes to predict CPT codes. CPT codes generated by the billing department were compared to those generated by our model. RESULTS: The random forest machine learning model had an AUC of .94 and an AUPRC of .85. The deep learning model had a final AUC of .72 and an AUPRC of .44. The random forest model had a weighted average, class-by-class accuracy of 87%. The LSTM deep learning model had a weighted average, class-by-class accuracy 0f 59%. CONCLUSIONS: Combining natural language processing with machine learning is a valid approach for automatic generation of CPT billing codes. The random forest machine learning model outperformed the LSTM deep learning model in this case. These models can be used by orthopedic or neurosurgery departments to allow for efficient billing.

Cervical disc arthroplasty: tips and tricks.

Cervical disc arthroplasty (CDA) is a powerful, motion-sparing treatment option for managing cervical radiculopathy or myelopathy. While CDA can be an excellent surgery for properly indicated patients, it is also less forgiving than cervical fusion. Optimally resolving patient symptoms while maintaining range of motion relies on near perfection in the surgical technique. Different CDA options exist on the market, with some having long-term proven success and others in early stages of clinical trials. We discuss the different options available for use, as well as strategies of positioning, approach, disc space preparation, implantation, and fusion prevention that we believe can help improve performance and outcomes of CDA.

Nutrient foramen location on the laminae provides a landmark for pedicle screw entry: a cadaveric study.

BACKGROUND: Nutrient foramina are often encountered around the entry point of pedicle screws. Further, while probing the pedicle for pedicle screw insertion around the nutrient foramen, bleeding from the probe insertion hole is often observed. The purpose of this study was to investigate the frequency of occurrence of nutrient foramina, the association between the nutrient foramen and pedicle, and the safety and accuracy of cervical and thoracic pedicle screw placement using the nutrient foramen as the entry point. METHODS: We identified the location of the nutrient foramina for the dorsal branches of the segmental artery and their anatomical association to the pedicles and bony landmarks in the vertebrae for C3-T12 in seven cadavers. We also determined the frequency with which the nutrient foramina were present in 119 cadaveric vertebrae. We identified the pedicle location, base of the superior articular facet, and lateral border of laminae with respect to the nutrient foramen. RESULTS: The overall presence of the nutrient foramina was 63% (150/238) in the specimens, with 60% (42/70) and 64% (108/168) identifiable in the cervical and thoracic vertebrae, respectively. In the cervical vertebrae, the nutrient foramen was located on the outer wall of the pedicle and was positioned between the cephalad and caudal walls. In the thoracic spine, 98% (106/108) nutrient foramina were located inside the pedicle walls. CONCLUSIONS: Our study findings confirm that the location of the nutrient foramen can be used for identifying the entry point for pedicle screws. In the cervical vertebrae, the nutrient foramina are located lateral to pedicle but within the cranial and caudal margins. In the thoracic vertebrae, the nutrient foramina are located in the medial and caudal regions of the pedicle. Thus, to decrease the risk of overshoot, the entry point for thoracic pedicle screws should be positioned a few millimeters cephalad and lateral to the nutrient foramen.