Maja Green

18 papers receiving 235 citations

Peers

Maja Green
Comparison fields: 5 of 55
  • Critical Care and Intensive Care Medicine 106
  • Anesthesiology and Pain Medicine 53
  • Developmental Neuroscience 27
  • Radiological and Ultrasound Technology 12
  • Health Informatics 3
Replace Chun-Mei Deng with:
Chun-Mei Deng China
Roman Kula Czechia
Hans Christian Boesen Denmark
Matthijs Plas Netherlands
Veerle De Sloovere Belgium
Felix Tiongco United States
Wan Mohd Nazaruddin Wan Hassan Malaysia
Jeffrey Zimering United States
Ralph Monfort United States
Dominique Leblanc Canada
Maja Green relative to Chun-Mei Deng China Chun-Mei Deng's profile →
Citations per field
00.5×7.7×
Chun-Mei Deng · 1×
Citations per year

Countries citing papers authored by Maja Green

Since Specialization
Citations

This map shows the geographic impact of Maja Green'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 Maja Green with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maja Green more than expected).

Fields of papers citing papers by Maja Green

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Maja Green. 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 Maja Green. The network helps show where Maja Green may publish in the future.

Co-authors

The 25 scholars most cited alongside Maja Green, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Maja Green Line = papers co-authored together Maja Green links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 2018138
2 202417
3 201115
4 201811
5 202510
6 20159
7 20188
8 20218
9 20225
10 20214
11 20194
12 20193
13 20253
14 20163
15 20223
16 20251
17 20251
18 20201
19 20250
20 20250

About Maja Green

Maja Green is a scholar working on Anesthesiology and Pain Medicine, Pharmacology, Molecular Biology, General Health Professions and Cognitive Neuroscience, having authored 20 papers that have together received 244 indexed citations. Recurring topics across this work include Pain Management and Treatment (3 papers), Treatment of Major Depression (3 papers), DNA Repair Mechanisms (2 papers), Intensive Care Unit Cognitive Disorders (2 papers), Cell death mechanisms and regulation (2 papers), Pain Management and Opioid Use (2 papers), Geriatric Care and Nursing Homes (2 papers) and Frailty in Older Adults (2 papers). The work is most often cited by research in Critical Care and Intensive Care Medicine (106 citations), Anesthesiology and Pain Medicine (53 citations), Developmental Neuroscience (27 citations), Radiological and Ultrasound Technology (12 citations) and Health Informatics (3 citations). Maja Green has collaborated with scholars based in Australia, United States and Malaysia. Frequent co-authors include Yahya Shehabi, Michael Bailey, Belinda Howe, Michael C. Reade, Anita Alias, Amartya Mukhopadhyay, S. A. R. Webb, Suhaini Kadiman, Lian Kah Ti and Rinaldo Bellomo. Their work appears in journals such as Journal of Pain Research, Health & Social Care in the Community, Pain Management, Progress in Neuro-Psychopharmacology and Biological Psychiatry and Critical Care Medicine.

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.

Explore authors with similar magnitude of impact