Neurosurgery
-
Labeling residents as "black" or "white" clouds based on perceived or presumed workloads is a timeworn custom across medical training and practice. Previous studies examining whether such perceptions align with objective workload patterns have offered conflicting results. We assessed whether such peer-assigned labels were associated with between-resident differences in objective, on-call workload metrics in three classes of neurosurgery junior residents. In doing so, we introduce more inclusive terminology for perceived differences in workload metrics. ⋯ Significant differences in objective on-call experience exist between junior neurosurgery residents. Self- and peer-assigned weather labels did not consistently align with a pattern of these differences, suggesting that other factors contribute to such labels.
-
The emergence of machine learning models has significantly improved the accuracy of surgical outcome predictions. This study aims to develop and validate an artificial neural network (ANN) model for predicting facial nerve (FN) outcomes after vestibular schwannoma (VS) surgery using the proximal-to-distal amplitude ratio (P/D) along with clinical variables. ⋯ ANN models incorporating P/D can be a valuable tool for predicting FN outcomes after VS surgery. Refining the model to include P/D with latencies between 6 and 8 ms further improves the model's prediction. A user-friendly interface is provided to facilitate the implementation of this model.
-
The role of stereotactic radiosurgery (SRS) in patients with brain metastases (BMs) from colorectal cancers (CRCs) has not been established. The authors present a single-institution experience of patients with CRC who underwent SRS with metastatic brain spread. ⋯ SRS effectively controls BMs from CRC with low risk of treatment-related toxicity. During follow-up, the development of additional metastases can be safely treated by repeat SRS.