Oncologic outcomes of retroperitoneal lymph node dissection following first-line chemotherapy for metastatic non-seminomatous germ cell tumors.

Background: Post-chemotherapy retroperitoneal lymph node dissection (pcRPLND) is integral to multimodal treatment of patients with metastatic non-seminomatous germ cell tumors (NSGCT). We review pathologic and long-term outcomes of pcRPLND following first-line chemotherapy with a focus on residual mass size and primary tumor histology. Our goal was to identify new predictive approaches that can refine surgical indications.

What are we missing? Data from the Gonorrhoea Undetected Resistance Laboratory Study (GURLS).

Objectives: Increasing ceftriaxone-resistant is of public health concern. A cluster of ceftriaxone-resistant was identified in 2022, which was linked to heterosexuals from the Asia-Pacific region who were studying at UK universities. The possibility of further transmission within and beyond this network was of concern, particularly as not all gonococcal cases have a positive culture for antimicrobial susceptibility testing to be performed.

Confirmatory Biopsy Outcomes in Patients with Grade Group 2 Prostate Cancer: Implications for Early Management.

Background And Objective: Guideline recommendations regarding early management of grade group (GG) 2 prostate cancer with confirmatory biopsy (cBx) are not well established. Our aim was to determine which patients with GG 2 cancer should undergo cBx before treatment decision-making by evaluating the probability of downgrading to GG 1 or no cancer on cBx.

Methods: This was a single-institution retrospective analysis of patients with GG 2 prostate cancer who underwent cBx.

Hypothesis: Net benefit as an objective function during development of machine learning algorithms for medical applications.

Net benefit is the most widely used metric for evaluating the clinical utility of medical prediction models. The approach applies decision analytic theory to weight true and false positives depending on the relative consequences of different decision outcomes. It is plausible that there are at least some machine learning scenarios where optimization of the objective function during model development will not optimize net benefit during model evaluation.