By Dr Sradha Srinivasa Kotwal

Preprints

Chan B; Varghese A; Badve SV; Pecoits-Filho R; Guedes M; Arnott C; Kozor R; O’Lone E; Jun M; Kotwal S; Block GA; Chertow GM; Solomon SD; Vaduganathan M; Neuen BL, 2024, Effects of iron on cardiovascular, kidney and safety outcomes in patients with chronic kidney disease: a systematic review and meta-analysis, http://dx.doi.org/10.1101/2024.03.28.24305010

Neuen B; Jun M; Wick J; Kotwal S; Badve S; Jardine M; Gallagher M; Chalmers J; Nallaiah K; Perkovic V; Peiris D; Rodgers A; Woodward M; Ronksley P, 2023, Estimating the population-level kidney benefits of improved uptake of SGLT2 inhibitors in patients with chronic kidney disease in Australian primary care, http://dx.doi.org/10.1101/2023.06.26.23291881

Jun M; Wick J; Neuen B; Kotwal S; Badve S; Woodward M; Chalmers J; Peiris D; Rodgers A; Nallaiah K; Jardine M; Perkovic V; Gallagher M; Ronksley P, 2023, The prevalence of chronic kidney disease in Australian primary care: analysis of a national general practice dataset, http://dx.doi.org/10.1101/2023.06.18.23290762

McGree JM; Hockham C; Kotwal S; Wilcox A; Bassi A; Pollock C; Burrell LM; Snelling T; Jha V; Jardine M; Jones M; for the CLARITY Trial Steering Committee , 2021, Controlled evaLuation of Angiotensin Receptor Blockers for COVID-19 respIraTorY disease (CLARITY): Statistical analysis plan for a randomised controlled Bayesian adaptive sample size trial, http://dx.doi.org/10.1101/2021.08.17.21262196

Barbieri S; Kemp J; Perez-Concha O; Kotwal S; Gallagher M; Ritchie A; Jorm L, 2019, Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk, http://dx.doi.org/10.48550/arxiv.1905.08547

Back to profile page