Mercè Ibarz

1.6k citations
45 papers · 967 · h-index 18

Impact in

Papers in

Mercè Ibarz

40 papers receiving 909 citations

Peers

Mercè Ibarz
Comparison fields: 5 of 104
  • Statistics, Probability and Uncertainty 402
  • Family Practice 59
  • Physiology 637
  • Medical Laboratory Technology 35
  • Nephrology 105
Replace Zorica Šumarac with:
Zorica Šumarac Serbia
Carmen Perich Spain
Joana Minchinela Spain
Virtudes Álvarez Spain
Thomas Røraas Norway
Alex Katayev United States
Una Ørvim Sølvik Norway
A. Uldall Denmark
Edward T. Wong United States
Fernando Cava Spain
Mercè Ibarz relative to Zorica Šumarac Serbia Zorica Šumarac's profile →
Citations per field
00.5×1.5×1.9×
Zorica Šumarac · 1×
Citations per year

Countries citing papers authored by Mercè Ibarz

Since Specialization
Citations

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

Fields of papers citing papers by Mercè Ibarz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 45 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2011142
2 2014105
3 201499
4 200781
5 201648
6 201147
7 201642
8 201840
9 201937
10 201835
11 200831
12 201627
13 201425
14 200921
15 201920
16 201620
17 200819
18 201018
19 201316
20 201813

About Mercè Ibarz

Mercè Ibarz is a scholar working on Physiology, Statistics, Probability and Uncertainty, General Health Professions, Nephrology and Surgery, having authored 45 papers that have together received 967 indexed citations. Recurring topics across this work include Clinical Laboratory Practices and Quality Control (28 papers), Meta-analysis and systematic reviews (17 papers), Healthcare cost, quality, practices (8 papers), Reliability and Agreement in Measurement (8 papers), Hemodynamic Monitoring and Therapy (5 papers), Parathyroid Disorders and Treatments (3 papers), Acute Kidney Injury Research (3 papers) and COVID-19 Clinical Research Studies (3 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (402 citations), Family Practice (59 citations), Physiology (637 citations), Medical Laboratory Technology (35 citations) and Nephrology (105 citations). Mercè Ibarz has collaborated with scholars based in Spain, United Kingdom and Italy. Frequent co-authors include Ana-Maria Šimundić, Michael P. Cornes, Giuseppe Lippi, Mads Nybo, Kjell Grankvist, Gunn B.B. Kristensen, José Manuel Valdivielso, Milica Božić, Petya Valcheva and Mario Plebani. Their work appears in journals such as Clinical Chemistry and Laboratory Medicine (CCLM), Biochemia Medica, Diagnosis, Nutrients and Clinical Kidney Journal.

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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