Luís Prieto

103 papers receiving 3.6k citations

Peers

Luís Prieto
Comparison fields: 5 of 161
  • Cardiology and Cardiovascular Medicine 406
  • Endocrinology, Diabetes and Metabolism 269
  • Obstetrics and Gynecology 117
  • Urology 89
  • Energy Engineering and Power Technology 47
Replace Tiejun Tong with:
Tiejun Tong Hong Kong
Walter Schmidt Germany
José M. Marı́n Spain
Seung Won Lee South Korea
Geert J. M. G. van der Heijden Netherlands
Alicja R. Rudnicka United Kingdom
Hee Jin Kang South Korea
Xiao Huang China
Paul Downey Ireland
Chyi‐Huey Bai Taiwan
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Citations per field
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Citations per year

Countries citing papers authored by Luís Prieto

Since Specialization
Citations

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

Fields of papers citing papers by Luís Prieto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Luís Prieto, 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 Luís Prieto Line = papers co-authored together Luís Prieto links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 113 papers — load more, or switch the sort, to bring in the rest.

#Work
1
[Population reference values of the Spanish version of the Health Questionnaire SF-36].
1998465
2 2010177
3
[Assessment of the reliability of clinical findings: the intraclass correlation coefficient].
1998177
4 2002150
5 2018145
6 2014134
7 2004111
8 2004104
9 2001103
10 1998102
11 201698
12 200896
13 201594
14 199787
15 199784
16 200278
17 198173
18 200572
19 200472
20 201468

About Luís Prieto

Luís Prieto is a scholar working on Physiology, Surgery, Cardiology and Cardiovascular Medicine, Artificial Intelligence and Genetics, having authored 113 papers that have together received 3.7k indexed citations. Recurring topics across this work include Atrial Fibrillation Management and Outcomes (9 papers), Asthma and respiratory diseases (9 papers), Energy Load and Power Forecasting (9 papers), Urological Disorders and Treatments (6 papers), Breast Cancer Treatment Studies (5 papers), Health Systems, Economic Evaluations, Quality of Life (5 papers), Estrogen and related hormone effects (5 papers) and Congenital Anomalies and Fetal Surgery (4 papers). The work is most often cited by research in Cardiology and Cardiovascular Medicine (406 citations), Endocrinology, Diabetes and Metabolism (269 citations), Obstetrics and Gynecology (117 citations), Urology (89 citations) and Energy Engineering and Power Technology (47 citations). Luís Prieto has collaborated with scholars based in Spain, United Kingdom and United States. Frequent co-authors include Sancho Salcedo‐Sanz, Jordi Alonso, Enrique Regidor, Luís de la Fuente, Gregorio Barrio, A. Portilla-Figueras, Ángel M. Pérez‐Bellido, Eva Bermejo, Ricardo García‐Herrera and Alfonso Casado. Their work appears in journals such as Journal of Asthma, Annals of Allergy Asthma & Immunology, Revista Española de Cardiología (English Edition), Journal of Hypertension and Journal of Clinical Oncology.

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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