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Honghai Ma

Researcher at Zhejiang University

Publications -  12
Citations -  217

Honghai Ma is an academic researcher from Zhejiang University. The author has contributed to research in topics: Internal medicine & Lung cancer. The author has an hindex of 5, co-authored 8 publications receiving 169 citations.

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Clinical Nomogram for Predicting Survival of Esophageal Cancer Patients after Esophagectomy.

TL;DR: The nomogram showed a clear prognostic superiority over the seventh AJCC-TNM classification and predicted the probabilities of 3- and 5-year survival, which corresponded closely with the actual survival rates.
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Indwelling versus Intermittent Urinary Catheterization following Total Joint Arthroplasty: A Systematic Review and Meta-Analysis.

TL;DR: In this paper, the authors conducted a meta-analysis of relevant randomized controlled trials (RCT) to compare the rates of UTI and postoperative urinary retention (POUR) in patients undergoing lower limb arthroplasty after either indwelling urinary catheterization or intermittent urinary catheters.
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Barbed versus traditional sutures for wound closure in knee arthroplasty: a systematic review and meta-analysis

TL;DR: A systematic review and meta-analysis indicates that use of KBS in KA reduces operative time and cost and KBS is the preferred option for wound closures, including arthrotomy and reattachment of subcutaneous and subcuticular tissues.
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Log Odds of Positive Lymph Nodes Predicts Survival in Patients After Resection for Esophageal Cancer.

TL;DR: LODDS predicts survival more accurately than either LNM or LNR in patients undergoing resection for esophageal cancer, especially for patients with an insufficient number of lymph nodes retrieved.
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Semi-quantitative Analysis of EBUS Elastography as a Feasible Approach in Diagnosing Mediastinal and Hilar Lymph Nodes of Lung Cancer Patients

TL;DR: It is suggested that elastography is a feasible technique that may safely help to predict LN metastasis during EBUS-TBNA and a clear BCP cutoff value is found to help define positive and negative LNs.