Lucinda Archer
- Surgery
- Epidemiology
- Artificial Intelligence top 10%
- Cardiology and Cardiovascular Medicine top 10%
- Pulmonary and Respiratory Medicine
- Co-authors
- Richard D RileyGary S. CollinsKym I E SnellJoie EnsorMaarten van SmedenThomas P. A. DebrayGlen P. MartinPaula Dhiman
- Topics
- Health Systems, Economic Evaluations, Quality of Life (10 papers)Machine Learning in Healthcare (8 papers)Meta-analysis and systematic reviews (8 papers)
- Journals
- Nature CommunicationsSHILAP Revista de lepidopterologíaBMJ
- Partner nations
- United KingdomNetherlandsBelgium
In The Last Decade
Lucinda Archer
27 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 126
- Surgery 274
- Epidemiology 198
- Artificial Intelligence 195
- Cardiology and Cardiovascular Medicine 170
- Pulmonary and Respiratory Medicine 129
Countries citing papers authored by Lucinda Archer
This map shows the geographic impact of Lucinda Archer'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 Lucinda Archer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lucinda Archer more than expected).
Fields of papers citing papers by Lucinda Archer
This network shows the impact of papers produced by Lucinda Archer. 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 Lucinda Archer. The network helps show where Lucinda Archer may publish in the future.
Co-authorship network of co-authors of Lucinda Archer
This figure shows the co-authorship network connecting the top 25 collaborators of Lucinda Archer. A scholar is included among the top collaborators of Lucinda Archer 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 Lucinda Archer. Lucinda Archer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 5 | |
| 3 | 2 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 6 | |
| 7 | 0 | |
| 8 | 6 | |
| 9 | Evaluation of clinical prediction models (part 2): how to undertake an external validation studybreakdown → | 102 |
| 10 | 0 | |
| 11 | 0 | |
| 12 | 1 | |
| 13 | 7 | |
| 14 | 2 | |
| 15 | 4 | |
| 16 | 18 | |
| 17 | 88 | |
| 18 | 56 | |
| 19 | 65 | |
| 20 | 76 |
About Lucinda Archer
Lucinda Archer is a scholar working on Health Informatics, Statistics, Probability and Uncertainty and Statistics and Probability, having authored 34 papers that have together received 1.2k indexed citations. Recurring topics across this work include Health Systems, Economic Evaluations, Quality of Life (10 papers), Machine Learning in Healthcare (8 papers) and Meta-analysis and systematic reviews (8 papers). The work is most often cited by research in Health Informatics (91 citations), Health Information Management (70 citations) and Geriatrics and Gerontology (53 citations). Lucinda Archer has collaborated with scholars based in United Kingdom, Netherlands and Belgium. Frequent co-authors include Richard D Riley, Gary S. Collins, Kym I E Snell, Joie Ensor, Maarten van Smeden, Thomas P. A. Debray, Glen P. Martin, Paula Dhiman, Matthew Sperrin and Ben Van Calster. Their work appears in journals such as Nature Communications, SHILAP Revista de lepidopterología and BMJ.
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.