Machine learning models predicting risk of revision or secondary knee injury after anterior cruciate ligament reconstruction demonstrate variable discriminatory and accuracy performance: a systematic review.

Background: To summarize the statistical performance of machine learning in predicting revision, secondary knee injury, or reoperations following anterior cruciate ligament reconstruction (ACLR), and to provide a general overview of the statistical performance of these models.

Methods: Three online databases (PubMed, MEDLINE, EMBASE) were searched from database inception to February 6, 2024, to identify literature on the use of machine learning to predict revision, secondary knee injury (e.g.

Comparative Efficacy of Adjuvant Nonopioid Analgesia in Adult Cardiac Surgical Patients: A Network Meta-Analysis.

Objectives: To compare the relative efficacy of adjuvant nonopioid analgesic regimens in adult cardiac surgical patients.

Design: This frequentist, random-effects network meta-analysis (NMA) was prospectively registered on PROSPERO (CRD42021282913) and conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analyses for Network Meta-Analyses (PRISMA-NMA). The risk of bias (RoB) and confidence of evidence were assessed by RoB 2 and Confidence in Network Meta-Analysis, respectively.