Articles: cations.
-
Women surgeons face numerous barriers to career advancement. Inequitable citation of surgical literature may represent a contributing factor to gender disparities in academic surgery. ⋯ Among top-tier surgical journals, publications by women-first and -last authors were less cited compared with publications by men-first and -last authors, but not among the highest-tier surgical journals. Gender bias may exist in the citation of surgical research, contributing to gender disparities in academic surgery.
-
Critical care nurse · Apr 2022
Reducing Ventilator-Associated Events: A Quality Improvement Project.
Mechanical ventilation is lifesaving therapy in intensive care units but can increase patients' risk for ventilator-associated events. These events are associated with longer intensive care unit and hospital stays, more ventilator days, and increased mortality rates. ⋯ The creation and implementation of clear, specific communication and processes for successfully managing patients receiving mechanical ventilation decreased the rate of ventilator-associated events.
-
The development of major low anterior resection syndrome (LARS) after low anterior resection is severely detrimental to quality of life, yet awareness of it by clinicians and patients and the frequency of treatment of LARS is unclear. ⋯ Major LARS is common yet seemingly underrecognized by clinicians because less than half of patients are on first-line therapy and practically none are on second- and third-line therapies. Long-term follow-up of patients after low anterior resection, improved preoperative and postoperative education, and continued symptom assessment is necessary to improve treatment of major LARS.
-
The American College of Surgeons (ACS) NSQIP risk calculator helps guide operative decision making. In patients with significant surgical risk, it may be unclear whether to proceed with "Hail Mary"-type interventions. To refine predictions, a local interpretable model-agnostic explanations machine (LIME) learning algorithm was explored to determine weighted patient-specific factors' contribution to mortality. ⋯ Through the application of machine learning algorithms (GBM and LIME), our model individualized predicted mortality and contributing factors with substantial ACS-NSQIP predicted mortality. USE of machine learning techniques may better inform operative decisions and family conversations in cases of significant surgical risk.