Study Design. Retrospective cohort study of prospectively accrued data. Objective. To evaluate a large, prospective, multicentre dataset of surgically treated degenerative cervical myelopathy (DCM) cases on the contemporary risk of C5 palsy with surgical approach. Summary of Background Data. The influence of surgical technique on postoperative C5 palsy after decompression for DCM is intensely debated. Comprehensive, covariate-adjusted analyses are needed using contemporary data. Methods. Patients with moderate to severe DCM were prospectively enrolled in the multicenter, randomized, Phase III CSM-Protect clinical trial and underwent either anterior or posterior decompression between Jan 31, 2012 and May 16, 2017. The primary outcome was the incidence of postoperative C5 palsy, defined as the onset of muscle weakness by at least one grade in manual muscle test at the C5 myotome with slight or absent sensory disruption after cervical surgery. Two comparative cohorts were made based on the anterior or posterior surgical approach. Multivariate hierarchical mixed-effects logistic regression was used to estimate odds ratios (OR) with 95% confidence intervals (CI) for C5 palsy. Results. A total of 283 patients were included, and 53.4% underwent posterior decompression. The total incidence of postoperative C5 palsy was 7.4% and was significantly higher in patients who underwent posterior decompression compared with anterior decompression (11.26% vs. 3.03%, P =0.008). After multivariable regression, the posterior approach was independently associated with greater than four times the likelihood of postoperative C5 palsy ( P =0.017). Rates of C5 palsy recovery were comparable between the two surgical approaches. Conclusion. The odds of postoperative C5 palsy are significantly higher after posterior decompression compared to anterior decompression for DCM. This may influence surgical decision-making when there is equipoise in deciding between anterior and posterior treatment options for DCM. Level of Evidence. Therapeutic Level—II
An Objective Structured Practical Examination (OSPE) is an effective and robust, but resource-intensive, means of evaluating anatomical knowledge. Since most OSPEs employ short answer or fill-in-the-blank style questions, the format requires many people familiar with the content to mark the exams. However, the increasing prevalence of online delivery for anatomy and physiology courses could result in students losing the OSPE practice that they would receive in face-to-face learning sessions. The purpose of this study was to test the accuracy of Decision Trees (DTs) in marking OSPE questions as a potential first step to creating an intelligent, online OSPE tutoring system. The study used the results of the winter 2020 semester final OSPE from McMaster University's anatomy and physiology course in the Faculty of Health Sciences (HTHSCI 2FF3/2LL3/1D06) as the data set. Ninety percent of the data set was used in a 10-fold validation algorithm to train a DT for each of the 54 questions. Each DT was comprised of unique words that appeared in correct, student-written answers. The remaining 10% of the data set was marked by the generated DTs. When the answers marked by the DT were compared to the answers marked by staff and faculty, the DT achieved an average accuracy of 94.49% across all 54 questions. This suggests that machine learning algorithms such as DTs are a highly effective option for OSPE grading and are suitable for the development of an intelligent, online OSPE tutoring system.
# CPSS-1. Abstract ID 108. Radiographic reporting in adolescent idiopathic scoliosis: Is there a discrepancy between radiologists’ reports and surgeons’ assessments? {#article-title-2} Cobb angle measurement is a standard method for quantification of scoliosis in patients with adolescent
Mechanism of injury is a largely understudied descriptor of acute traumatic spinal cord injury (tSCI). This study sought to compare the impact of high-energy and low-energy mechanisms of injury in neurological outcomes of cervical sensorimotor complete tSCI.Patients with tSCI were identified in 4 prospective, multicenter clinical trials and registries. American Spinal Injury Association Impairment Scale (AIS) grade was assessed ≤ 72 hours postinjury and followed up between 12 to 52 weeks. Patients were included if they had a cervical and sensorimotor complete (AIS-A) injury at baseline. Study outcomes were change in AIS grade and lower extremity motor, upper extremity motor, and total motor scores. Propensity score matching between high-energy mechanisms of injury (HEMI; e.g. , motor vehicle collisions) and low-energy mechanisms of injury (LEMI; e.g. , falls) groups was performed. Adjusted groups were compared with paired t-tests and McNemar test.Of 667 patients eligible for inclusion, 523 experienced HEMI (78.4%). HEMI patients were younger, had lower body mass index, more associated fractures or dislocations, and lower baseline lower extremity motor scores. After propensity score matching of these baseline variables, 118 pairs were matched. HEMI patients had a significantly worse motor recovery from baseline to follow-up based on their diminished change in upper extremity motor scores and total motor scores.Cervical sensorimotor complete tSCIs from HEMI were associated with significantly lower motor recovery compared to LEMI patients. Our findings suggest that mechanism of injury should be considered in modelling prognosis and in understanding the heterogeneity of outcomes after acute tSCI.
INTRODUCTION: Degenerative Cervical Myelopathy (DCM) is the leading cause of disability secondary to a spinal pathology in adults. There exists clinical equipoise between surgery and conservative management with monitoring for patients with mild DCM. METHODS: A harmonized dataset was developed from the AOSpine North America, AOSpine International datasets as well as the CSM-Protect trial (N:1047). DCM patients (mJOA ≥15) were selected from the harmonized dataset. Latent class trajectory modeling was applied to classify patients into distinct trajectories. The optimal number of trajectories were chosen based on i) least Bayesian Information Criterion; ii) posterior probability >0.70; iii) odds of correct classification >5; and concordance between the estimated and actual proportion of patients assigned to a class. Predictors of recovery trajectories were identified using descriptive statistics and logistic regression on baseline variables. RESULTS: Two distinct recovery trajectories were revealed from our analysis. Good recovery trajectory had patients that improved to near-maximal mJOA scores within 12 months. Functional decline trajectory was characterized by a decline in more than two mJOA points over 12 months. There were 33 patients (15.6%) that followed functional decline and 179 that showed good recovery (84.4%). Demographic factors associated with following a functional decline trajectory included older age, hypertension, and higher baseline Nurick score. Posterior laminectomy and fusion was associated with functional decline compared to anterior fixation. CONCLUSIONS: Mild DCM can be classified into one of two distinct subpopulations with different recovery trajectories. This study lends support to the heterogeneity of mild DCM and classifying the clinical course of mild DCM based on defined clinical phenotypes rather than baseline severity of myelopathy alone. We also discovered that anterior surgery is associated with better neurological recovery in the context of mild DCM patients.
BACKGROUND AND OBJECTIVES: Interhospital transfer from community hospitals to centers specialized in managing traumatically injured individuals can strain patients, healthcare systems, and delay appropriate care. The purpose was to compare long-term neurological outcomes in transferred or directly admitted patients with traumatic spinal cord injury (SCI). METHODS: An ambispective cohort study was conducted using prospectively collected data (between 2005 and 2018) from 11 specialized level 1 trauma centers across the United States and Canada. All patients who underwent surgical management for SCI were included and placed into 2 comparison cohorts: (1) direct admission and (2) transfer from intermediate hospital. Outcomes were change in American Spinal Injury Association Impairment Scale grade and its components: upper-extremity motor, lower-extremity motor, pinprick, and light touch scores from baseline (assessed ≤72 hours after injury) to follow-up (12-52 weeks). Nearest-neighbor 1:1 propensity score matching between the transferred and directly admitted cohorts was performed. Paired analysis using McNemar's test and paired Student's t -test was used to determine the extent of the difference in neurological outcomes. RESULTS: Nine hundred seventy patients (55.5% male, 55.2 ± 18.9 years) with traumatic SCI were directly admitted to a specialized trauma center (N = 474, 48.9%) or transferred from an intermediate hospital (N = 496, 51.1%). After propensity score matching, 283 pairs were matched. Compared with a matched cohort of transferred patients, American Spinal Injury Association Impairment Scale grade improved more in directly admitted patients (56.2% vs 46.3%, P = .024), as did upper-extremity motor score (13.7 ± 12.8 vs 10.4 ± 11.5, P = .018) and light touch score (22.0 ± 29.7 vs 16.9 ± 26.6, P = .034). CONCLUSION: Patients with SCI directly admitted to specialized trauma centers have greater neurological recovery compared with patients transferred from an intermediate hospital. Feasibility of direct admission to a center specialized in the management of acute SCI through implementation of a standardized code program must be further investigated. LEVEL OF EVIDENCE: Therapeutic level II.
In a shift of medical education to a competency-based curriculum, practical examinations (PEs) are an effective but resource-intensive method of evaluating anatomy students. The short answer format of PEs requires evaluators familiar with the content to mark the exams. Moreover, the increasing transition to online anatomy courses could result in students losing the PE practice they would receive during in-person sessions. By virtue of the technical and close-ended nature of typical PE answers as well as grading usually being a binary ‘correct’ or ‘incorrect’ classification with no partial credit, it was hypothesized that the limited lexicon would allow for accurate grading using artificial intelligence disciplinessuch as natural language processing and decision trees (DTs). This research was done as a first step towards making an intelligent tutoring system for anatomy students. The study used the winter semester online PE results (n = 371) from McMaster University Faculty of Health Sciences’ anatomy and physiology course as the data set. For each of the 54 questions, a 10-fold cross-validation process was used where 90% of the answers (training set) trained the DT. After removing common words unrelated to correctness (“the”, “a”, “an”, etc.), each DT was comprised of unique words that appeared in student answers in a tree-like structure of nodes. Each node has an associated word as well as a correct/incorrect classification label and splits into sub-nodes (creating the tree-like structure). The remaining 10% of the answers (testing set), was marked by the generated DTs by traversing the tree starting from the top-most node. After traversing the tree, the classification label of the final node became the grade for the student's answer. Accuracy for each question was calculated as the number of proper classifications by the algorithm over the total number of answers. When the answer marked by the DT were compared to the answers marked by staff and faculty, the DT achieved an average of 94.49% accuracy in grading every non-blank student answer across all 54 questions. It was found that accuracy was negatively correlated to the number of unique words in the set of answers (-0.71, p<0.07), which was consistent with the initial hypothesis. As features such as spellchecking were not included in the algorithm to reduce the number of variables, the current results may underestimate the effectiveness of automated PE grading by DTs. The accuracy attained by the algorithms suggests that machine learning algorithms such as NLP and DTs may be used to reduce the workload of manual PE grading by instructional staff and mark a step towards developing an intelligent online PE tutoring system for anatomy.
