David Pilcher

20.8k total citations · 6 hit papers
364 papers, 12.5k citations indexed

About

David Pilcher is a scholar working on Epidemiology, Emergency Medicine and Surgery. According to data from OpenAlex, David Pilcher has authored 364 papers receiving a total of 12.5k indexed citations (citations by other indexed papers that have themselves been cited), including 141 papers in Epidemiology, 123 papers in Emergency Medicine and 93 papers in Surgery. Recurrent topics in David Pilcher's work include Sepsis Diagnosis and Treatment (113 papers), Cardiac Arrest and Resuscitation (69 papers) and Emergency and Acute Care Studies (62 papers). David Pilcher is often cited by papers focused on Sepsis Diagnosis and Treatment (113 papers), Cardiac Arrest and Resuscitation (69 papers) and Emergency and Acute Care Studies (62 papers). David Pilcher collaborates with scholars based in Australia, New Zealand and United States. David Pilcher's co-authors include Michael Bailey, Rinaldo Bellomo, D. James Cooper, Kirsi‐Maija Kaukonen, Satoshi Suzuki, Carlos Scheinkestel, Vincent Pellegrino, Carol Hodgson, Matthieu Schmidt and Andrew Udy and has published in prestigious journals such as New England Journal of Medicine, JAMA and Nature Communications.

In The Last Decade

David Pilcher

336 papers receiving 12.2k citations

Hit Papers

Mortality Related to Severe Sepsis and Septic Shock Among... 2014 2026 2018 2022 2014 2017 2015 2015 2014 400 800 1.2k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
David Pilcher Australia 50 4.6k 3.9k 3.2k 2.7k 2.7k 364 12.5k
Kathy Rowan United Kingdom 64 5.2k 1.1× 4.2k 1.1× 4.0k 1.3× 3.4k 1.2× 901 0.3× 281 16.2k
Janice L. Zimmerman United States 29 4.4k 1.0× 4.0k 1.0× 1.9k 0.6× 2.4k 0.9× 953 0.4× 71 10.8k
Daniel Talmor United States 59 3.7k 0.8× 4.1k 1.1× 2.4k 0.8× 4.1k 1.5× 1.5k 0.6× 233 13.9k
Lena M. Napolitano United States 60 4.3k 0.9× 3.6k 0.9× 5.9k 1.8× 4.3k 1.6× 792 0.3× 277 17.2k
Laurent Papazian France 66 4.3k 0.9× 3.1k 0.8× 1.9k 0.6× 5.5k 2.0× 1.6k 0.6× 289 15.9k
David A Harrison United Kingdom 62 6.1k 1.3× 3.3k 0.9× 4.6k 1.5× 2.7k 1.0× 717 0.3× 281 14.9k
Ognjen Gajic United States 61 3.8k 0.8× 2.8k 0.7× 2.1k 0.7× 3.4k 1.3× 697 0.3× 358 11.5k
Simon Finfer Australia 52 5.4k 1.2× 2.4k 0.6× 2.7k 0.8× 4.2k 1.5× 802 0.3× 210 13.1k
Joseph A. Carcillo United States 63 9.5k 2.1× 2.9k 0.7× 3.0k 0.9× 3.7k 1.3× 1.2k 0.4× 291 20.8k
Emanuel P. Rivers United States 40 7.8k 1.7× 3.1k 0.8× 4.5k 1.4× 3.1k 1.2× 932 0.3× 127 12.4k

Countries citing papers authored by David Pilcher

Since Specialization
Citations

This map shows the geographic impact of David Pilcher's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Pilcher with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Pilcher more than expected).

Fields of papers citing papers by David Pilcher

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Pilcher. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Pilcher. The network helps show where David Pilcher may publish in the future.

Co-authorship network of co-authors of David Pilcher

This figure shows the co-authorship network connecting the top 25 collaborators of David Pilcher. A scholar is included among the top collaborators of David Pilcher based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Pilcher. David Pilcher is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
3.
Dendle, Claire, Monica A. Slavin, Robert Weinkove, et al.. (2024). Neutropenic Sepsis in the Intensive Care Unit: Differences in Clinical Profile and Outcomes According to the Cause of Neutropenia. Open Forum Infectious Diseases. 11(6). ofae289–ofae289. 1 indexed citations
4.
Ziegler, Jennifer, David Pilcher, Rinaldo Bellomo, et al.. (2024). Epidemiology of Renal Replacement Therapy for Critically Ill Patients across Seven Health Jurisdictions. American Journal of Nephrology. 55(5). 539–550. 2 indexed citations
5.
Bloom, Jason, E. Paratz, L. Dawson, et al.. (2024). Healthcare and economic cost burden of emergency medical services treated non-traumatic shock using a population-based cohort in Victoria, Australia. BMJ Open. 14(4). e078435–e078435. 2 indexed citations
6.
Stephens, Andrew F., David Pilcher, Ryan P. Barbaro, et al.. (2023). ECMO PAL: using deep neural networks for survival prediction in venoarterial extracorporeal membrane oxygenation. Intensive Care Medicine. 49(9). 1090–1099. 20 indexed citations
8.
Tiruvoipati, Ravindranath, Ary Serpa Neto, John Wilson, et al.. (2021). An Exploratory Analysis of the Association between Hypercapnia and Hospital Mortality in Critically Ill Patients with Sepsis. Annals of the American Thoracic Society. 19(2). 245–254. 11 indexed citations
9.
Pilcher, David, et al.. (2021). Does equipoise exist amongst experts regarding the role of hyperbaric oxygen treatment for necrotising soft tissue infection?. ANZ Journal of Surgery. 91(4). 485–487. 1 indexed citations
10.
Wilson, Anthony, et al.. (2021). Comparison of Intensive Care and Trauma-specific Scoring Systems in Critically Ill Patients. Injury. 52(9). 2543–2550. 8 indexed citations
11.
Snell, Gregory I., Mark McDonald, Rohit D’Costa, et al.. (2021). Improving the predictability of time to death in controlled donation after circulatory death lung donors. Transplant International. 34(5). 906–915. 5 indexed citations
13.
Pilcher, David, Edward Litton, Johnny Millar, et al.. (2018). In-hospital cardiac arrests: events worth monitoring?. Critical Care and Resuscitation. 20(4). 321–321. 1 indexed citations
14.
Bailey, Michael, Rinaldo Bellomo, Peter Stow, et al.. (2016). Insurance status and mortality in critically ill patients. Critical Care and Resuscitation. 18(1). 43–e11. 9 indexed citations
16.
Santamaria, John, Graeme Duke, David Pilcher, et al.. (2015). The Timing of Discharge from the Intensive Care Unit and Subsequent Mortality. A Prospective, Multicenter Study. American Journal of Respiratory and Critical Care Medicine. 191(9). 1033–1039. 36 indexed citations
17.
Schmidt, Matthieu, Michael Bailey, Jayne Sheldrake, et al.. (2014). Predicting Survival after Extracorporeal Membrane Oxygenation for Severe Acute Respiratory Failure. The Respiratory Extracorporeal Membrane Oxygenation Survival Prediction (RESP) Score. American Journal of Respiratory and Critical Care Medicine. 189(11). 1374–1382. 500 indexed citations breakdown →
18.
Schneider, Antoine, Miklós Lipcsey, Michael Bailey, David Pilcher, & Rinaldo Bellomo. (2012). Relationship between illness severity scores in acute kidney injury. Critical Care and Resuscitation. 14(1). 53–55. 6 indexed citations
19.
Miles, Lachlan F., Michael Bailey, Paul J. Young, & David Pilcher. (2012). Differences in mortality based on worsening ratio of partial pressure of oxygen to fraction of inspired oxygen corrected for immune system status and respiratory support. Critical Care and Resuscitation. 14(1). 25–32. 4 indexed citations
20.
McNamee, James J., David Pilcher, Michael Bailey, Edwina C. Moore, & Heather Cleland. (2010). Mortality prediction and outcomes among burns patients from Australian and New Zealand intensive care units. Critical Care and Resuscitation. 12(3). 196–201. 11 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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