Predicting Reduction Mammaplasty Total Resection Weight With Machine Learning.

BACKGROUND: Machine learning (ML) is a form of artificial intelligence that has been used to create better predictive models in medicine. Using ML algorithms, we sought to create a predictive model for breast resection weight based on anthropometric measurements. METHODS: We analyzed 237 patients (474 individual breasts) who underwent reduction mammoplasty at our institution. Anthropometric variables included body surface area (BSA), body mass index, sternal notch-to-nipple (SN-N), and nipple-to-inframammary fold values. Four different ML algorithms (linear regression, ridge regression, support vector regression, and random forest regression) either including or excluding the Schnur Scale prediction for the same data were trained and tested on their ability to recognize the relationship between the anthropometric variables and total resection weights. Resection weight prediction accuracy for each model and the Schnur scale alone were evaluated based on using mean absolute error (MAE). RESULTS: In our cohort, mean age was 40.36 years. Most patients (71.61%) were African American. Mean BSA was 2.0 m 2 , mean body mass index was 33.045 kg/m 2 , mean SN-N was 35.0 cm, and mean nipple-to-inframammary fold was 16.0 cm. Mean SN-N was found to have the greatest variable importance. All 4 models made resection weight predictions with MAE lower than that of the Schnur Scale alone in both the training and testing datasets. Overall, the random forest regression model without Schnur scale weight had the lowest MAE at 186.20. CONCLUSION: Our ML resection weight prediction model represents an accurate and promising alternative to the Schnur Scale in the setting of reduction mammaplasty consultations.

Tendon Transfers for Knee Extension Following Femoral Nerve Injury After Hip Arthroplasty.

Femoral nerve injury is a rare but devastating complication of direct anterior approach total hip arthroplasty that occurs in about 1% of the cases and could potentially lead to debilitating loss of knee extension. In this case report, we present a case of femoral nerve injury following direct anterior approach hip arthroplasty with an inability to extend the affected knee, gait instability, and multiple falls. For this patient, an innovative functional adductor magnus muscle transfer was performed to restore knee extension. At 6 months after surgery, the patient's knee extension was partly restored, and ambulation was significantly improved.

Trends of medicare reimbursement for breast cancer and reconstructive surgery.

This study evaluated trends in Medicare reimbursement for commonly performed breast oncologic and reconstructive procedures. Average national relative value units (RVUs) for physician-based work, facilities, and malpractice were collected along with the corresponding conversion factors for each year. From 2010 to 2021, there was an overall average decrease of 15% in Medicare reimbursement for both breast oncology (-11%) and reconstructive procedures (-16%). Based on these findings, breast and reconstructive surgeons should advocate for reimbursement that better reflects the costs of their practice.