Publications by authors named "A D Altman"

Objective: To determine if anaemia and blood transfusions in the perioperative, chemotherapy and radiation treatment periods are associated with overall survival (OS) and recurrence-free survival (RFS) in high-grade endometrial cancer.

Methods: This retrospective cohort study examined patients at a single centre treated for high-grade endometrial cancer (2010-2023). This included International Federation of Gynecology and Obstetrics (FIGO) grade 3 endometrioid, serous, carcinosarcoma, mixed, clear cell, mucinous, dedifferentiated and undifferentiated histology.

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Objective: To evaluate whether Robotic or Laparoscopic Nissen Fundoplication (LNF) improves voice outcomes and symptoms in patients with Laryngopharyngeal Reflux (LPR) compared to patients who were candidates for surgery but elected to receive treatment with antireflux medical management alone.

Study Design: Retrospective chart review.

Methods: A retrospective chart review was conducted of patients who visited the office of the senior author, received a diagnosis of LPR, and were candidates for LNF.

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•First reported case of Docetaxel Induced Myositis in Uterine Leiomyosarcoma treatment.•Docetaxel Induced Myositis diagnosed muscle pain and weakness, elevated CK and imaging.•Treatment with corticosteroids and physiotherapy showed recovery in 3 months.

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Objective: We assessed the global distribution and academic, administrative and research outcomes of international fellows (IFs) trained in Canadian gynecologic oncology (GO) programs.

Methods: A web-based survey was sent to IFs who completed GO training in Canada. Using the Web of science database, we identified the publication list, citation record and H-index of IFs and classified them according to their region of practice: high-income countries (HIC), middle income countries (MIC), and low-income countries (LIC).

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Powder X-ray diffraction (PXRD) is a cornerstone technique in materials characterization. However, complete structure determination from PXRD patterns alone remains time-consuming and is often intractable, especially for novel materials. Current machine learning (ML) approaches to PXRD analysis predict only a subset of the total information that comprises a crystal structure.

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