Publications by authors named "J M Monson"

Background: The number of meniscal repairs being completed each year is increasing; however, the optimal, cost-effective postoperative assessment to determine the success or failure of a meniscal repair is not well known.

Purpose/hypothesis: The purpose of this systematic review was to identify the clinical examination testing that correlates with objective magnetic resonance imaging (MRI) or second-look arthroscopy (SLA) findings to determine an optimal clinical workup for assessing postoperative meniscal repair healing. It was hypothesized that specific clinical tests would correlate with meniscal repairs that did not heal.

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Tears of the posterior medial meniscus root (PMMR) are common in older patients and reportedly contribute to rapid joint degeneration over time. Recognition of these tear types and the appropriate diagnosis through clinical exam and diagnostic imaging have improved significantly in recent years, as have surgical techniques to address them. Standardized post-operative rehabilitation protocols specific to PMMR repair have not been established or well understood in the scientific literature.

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Purpose: To examine the role of lower extremity blood flow restriction (BFR) in the athletic population.

Methods: This study was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement guidelines. Searches of Level I and II studies were performed on PubMed, Embase, and Cochrane databases.

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Background: The use of a watch-and-wait management strategy after a complete clinical response to neoadjuvant therapy for rectal cancer is increasing. However, insights into implementation, treatments, and outcomes on a national level in the United States are limited.

Objective: To investigate and report on watch-and-wait management practices and outcomes in the United States.

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Purpose: Current clinical risk stratification methods for localized prostate cancer are suboptimal, leading to over- and undertreatment. Recently, machine learning approaches using digital histopathology have shown superior prognostic ability in phase III trials. This study aims to develop a clinically usable risk grouping system using multimodal artificial intelligence (MMAI) models that outperform current National Comprehensive Cancer Network (NCCN) risk groups.

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