Background: Knowledge is limited about the diagnosis and treatment of modified Gartland type-IV supracondylar humeral fractures. We determined the prevalence of type-IV fractures, identified preoperative characteristics associated with these injuries, and assessed operative treatment characteristics. Methods: We retrospectively identified patients <16 years of age who underwent operative treatment of a supracondylar humeral fracture at 2 centers between 2008 and 2016. We compared patient, injury, and treatment characteristics between type-IV and type-III fracture groups (1:4, cases:controls). Preoperative radiographs were assessed by 4 pediatric orthopaedists blinded to fracture type. The odds of a fracture being type IV were assessed using univariate logistic regression for individual radiographic parameters. Significance was set at alpha = 0.05. Results: Type-IV fractures accounted for 39 (1.3%) of the supracondylar humeral fractures treated operatively during the study period. A type-IV fracture was associated with the following radiographic parameters: flexion angulation (odds ratio [OR] = 17; 95% confidence interval [CI] = 4.9 to 59), valgus angulation (OR = 5.6; 95% CI = 1.6 to 20), and lateral translation (OR = 4.1; 95% CI = 1.6 to 11) of the distal fragment; osseous apposition between the proximal and distal fragments (OR = 4.0; 95% CI = 1.8 to 9.0); and propagation of the fracture line toward the diaphysis of the proximal segment (OR = 9.2; 95% CI = 1.6 to 53). We found no significant differences in patient or injury characteristics between the groups. Compared with type-III fractures, type-IV fractures were treated more frequently with open reduction and percutaneous pinning (13% compared with 3.8%; p = 0.04) and were associated with longer mean operative time (82 ± 42 compared with 63 ± 28 minutes; p = 0.001). Conclusions: We identified 5 preoperative radiographic parameters associated with greater odds of a supracondylar humeral fracture being type IV rather than type III. No patient or injury characteristic differed significantly between the groups. Substantial overlap likely exists between type-IV and flexion-type fractures. Type-IV fractures were associated with longer operative time and were treated with open reduction more frequently than were type-III fractures. Level of Evidence: Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.

Introduction: Publicly available AI language models such as ChatGPT have demonstrated utility in text generation and even problem-solving when provided with clear instructions. Amidst this transformative shift, the aim of this study is to assess ChatGPT's performance on the orthopaedic surgery in-training examination (OITE). Methods: All 213 OITE 2021 web-based questions were retrieved from the AAOS-ResStudy website (https://www.aaos.org/education/examinations/ResStudy). Two independent reviewers copied and pasted the questions and response options into ChatGPT Plus (version 4.0) and recorded the generated answers. All media-containing questions were flagged and carefully examined. Twelve OITE media-containing questions that relied purely on images (clinical pictures, radiographs, MRIs, CT scans) and could not be rationalized from the clinical presentation were excluded. Cohen's Kappa coefficient was used to examine the agreement of ChatGPT-generated responses between reviewers. Descriptive statistics were used to summarize the performance (% correct) of ChatGPT Plus. The 2021 norm table was used to compare ChatGPT Plus' performance on the OITE to national orthopaedic surgery residents in that same year. Results: A total of 201 questions were evaluated by ChatGPT Plus. Excellent agreement was observed between raters for the 201 ChatGPT-generated responses, with a Cohen's Kappa coefficient of 0.947. 45.8% (92/201) were media-containing questions. ChatGPT had an average overall score of 61.2% (123/201). Its score was 64.2% (70/109) on non-media questions. When compared to the performance of all national orthopaedic surgery residents in 2021, ChatGPT Plus performed at the level of an average PGY3. Discussion: ChatGPT Plus is able to pass the OITE with an overall score of 61.2%, ranking at the level of a third-year orthopaedic surgery resident. It provided logical reasoning and justifications that may help residents improve their understanding of OITE cases and general orthopaedic principles. Further studies are still needed to examine their efficacy and impact on long-term learning and OITE/ABOS performance.

Study Design.: Retrospective, case–control. Objective.: The aim of this study was to use predictive modeling and machine learning to develop novel tools for identifying patients who may be appropriate for single-level outpatient anterior cervical fusion and discectomy (ACDF), and to compare these to legacy metrics. Summary of Background Data.: ACDF performed in an ambulatory surgical setting has started to gain popularity in recent years. Currently there are no standardized risk-stratification tools for determining which patients may be safe candidates for outpatient ACDF. Methods.: Adult patients with American Society of Anesthesiologists (ASA) Class 1, 2, or 3 undergoing one-level ACDF in inpatient or outpatient settings were identified in the National Surgical Quality Improvement Program database. Patients were deemed as “unsafe” for outpatient surgery if they suffered any complication within a week of the index operation. Two different methodologies were used to identify unsafe candidates: a novel predictive model derived from multivariable logistic regression of significant risk factors, and an artificial neural network (ANN) using preoperative variables. Both methods were trained using randomly split 70% of the dataset and validated on the remaining 30%. The methods were compared against legacy risk-stratification measures: ASA and Charlson Comorbidity Index (CCI) using area under the curve (AUC) statistic. Results.: A total of 12,492 patients who underwent single-level ACDF met the study criteria. Of these, 9.79% (1223) were deemed unsafe for outpatient ACDF given development of a complication within 1 week of the index operation. The five clinical variables that were found to be significant in the multivariable predictive model were: advanced age, low hemoglobin, high international normalized ratio, low albumin, and poor functional status. The predictive model had an AUC of 0.757, which was significantly higher than the AUC of both ASA (0.66; P < 0.001) and CCI (0.60; P < 0.001). The ANN exhibited an AUC of 0.740, which was significantly higher than the AUCs of ASA and CCI (all, P < 0.05), and comparable to that of the predictive model (P > 0.05). Conclusion.: Predictive analytics and machine learning can be leveraged to aid in identification of patients who may be safe candidates for single-level outpatient ACDF. Surgeons and perioperative teams may find these tools useful to augment clinical decision-making. Level of Evidence: 3

Background Cryoballoon ablation could induce stricture formation to achieve outlet and pouch reduction in patients regaining weight after Roux-en-Y gastric bypass (RYGB). This pilot study aimed to assess technical feasibility and short-term efficacy. Methods A retrospective chart review (January – November 2018) at two academic centers identified patients with weight regain post-RYGB, treated with cryoablation if pouch > 4 cm and/or outlet > 15 mm. Patients were scheduled for surveillance endoscopies at 8 weeks. Results 22 patients presented 10.5 years (SD 4.42) post-RYGB with weight regain of 30.9 kg (SD 13.7). Technical success was 89.5 % for outlet ablation and 93.0 % for pouch ablation. From baseline to 8 weeks, the outlet was reduced from 24.1 mm (95 % confidence interval [CI] 19.8 to 28.5) to 17.1 mm (95 %CI 13.1 to 21.1; P < 0.001), and pouch from 5 cm (95 %CI 4.1 to 5.9) to 3.9 cm (95 %CI 2.6 to 5.1; P < 0.05). Total body weight loss at 8 weeks was 8.1 % (SD 12.8 %). Conclusion Cryoablation appears technically feasible and effective for outlet and/or pouch reduction in the short term.

Background: There exists a substantial risk of having a perioperative complication after revision total joint arthroplasty (TJA). The complex shared decision-making between surgeon and patient would benefit from a high-fidelity tool to identify patients at risk for mortality after revision TJA. Therefore, we developed the CARDE-B score. CARDE-B is an acronym for congestive heart failure, albumin or malnutrition (65 years of age), and body mass index <25 kg/m². We developed and validated the CARDE-B score to determine the risk of death within 30 days of a revision TJA. Methods: A total of 13,118 revision TJAs (40% hip and 60% knee) from the National Surgical Quality Improvement Program (NSQIP) database were analyzed. A simple 1-point scoring system, CARDE-B, was created for predicting 30-day mortality after revision TJA, based on a logistic regression model. The CARDE-B scoring system assigns 1 point to each criterion in the acronym: congestive heart failure, albumin (65 years of age), and body mass index of <25 kg/m2. The CARDE-B scoring system was compared with 2 commonly utilized scores: American Society of Anesthesiologists (ASA) physical status classification and the 5-factor modified frailty index (mFI-5). The area under the curve (AUC) was used to assess the accuracy of each model. The Hosmer-Lemeshow test was used to assess goodness of fit. Finally, the Nationwide Inpatient Sample (NIS) was used for external validation of the CARDE-B score in 19,153 patients who underwent revision TJA in 2017. Results: Eighty-eight patients (0.7%) did not survive 30 days after revision TJA. The AUC for the logistic regression model was 0.88 in both the derivation and internal validation samples using NSQIP. The predicted probability of 30-day mortality after revision TJA increased stepwise from <0.01% for a CARDE-B score of 0 points to 39% for a CARDE-B score of 5 points. The AUC for the CARDE-B score predicting 30-day mortality after revision TJA was 0.85. This was more accurate (p < 0.001) than the ASA physical status classification (AUC, 0.77) and the mFI-5 (AUC, 0.67). The AUC for the CARDE-B score in the NIS external validation set was 0.75. The Hosmer-Lemeshow p value for goodness of fit was 0.34, indicating goodness of fit in the external validation sample. Conclusions: The CARDE-B score is a simple system that predicts the risk of death within 30 days of a revision TJA, offering surgeons and patients a valuable and validated risk-stratification tool. Level of Evidence: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.