Introduction:On March 30, 2022, the American Orthopaedic Association's Council of Orthopaedic Residency Directors announced its endorsement for a preference signaling program (PSP) for the 2022 to 2023 orthopaedic residency application cycle. The purpose of our study was to assess orthopaedic surgery residency program director (PD) perceptions of the PSP and analyze potential effects of the PSP on the residency application process.Methods:A 19-question survey was distributed to 98 PDs (40.8% response rate). Contact information was obtained from a national database.Results:Most programs plan to participate in the PSP (87.5%). Preference signaling is highly regarded for residency selection, with PDs ranking its relative importance just below away rotation performance and personal knowledge of the applicant. Most PDs agreed that applicants will have increased chances of receiving interviews at programs they send a preference signal (65%). Most PDs also do not think that the PSP will help improve diversity (42.5%) and combat the overapplication phenomenon (67.5%). A majority think that an application cap limiting the total number of applications submitted should be initiated in future application cycles (85%).Conclusion:Preference signaling will be one of the most important factors considered during orthopaedic residency selection. A signal will likely improve applicants' chance of receiving an interview. Thus, students should be selective about where they send their preference signals and invest time in creating strong, personal connections with a few, select programs to increase their success in the orthopaedic residency match.
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.
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