Martin Májovský

872 total citations
41 papers, 478 citations indexed

About

Martin Májovský is a scholar working on Surgery, Endocrinology, Diabetes and Metabolism and Genetics. According to data from OpenAlex, Martin Májovský has authored 41 papers receiving a total of 478 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Surgery, 15 papers in Endocrinology, Diabetes and Metabolism and 11 papers in Genetics. Recurrent topics in Martin Májovský's work include Pituitary Gland Disorders and Treatments (15 papers), Glioma Diagnosis and Treatment (11 papers) and Cerebrospinal fluid and hydrocephalus (9 papers). Martin Májovský is often cited by papers focused on Pituitary Gland Disorders and Treatments (15 papers), Glioma Diagnosis and Treatment (11 papers) and Cerebrospinal fluid and hydrocephalus (9 papers). Martin Májovský collaborates with scholars based in Czechia, France and United Kingdom. Martin Májovský's co-authors include David Netuka, Vladimír Beneš, Martin Černý, Martin Komarc, Václav Masopust, Ondřej Bradáč, Jaromı́r Astl, Patricia de Lacy, Pavel Kučera and Lenka Pospíšilová and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Journal of neurosurgery.

In The Last Decade

Martin Májovský

36 papers receiving 472 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Martin Májovský Czechia 13 138 110 109 108 95 41 478
Aislyn C. DiRisio United States 11 104 0.8× 11 0.1× 34 0.3× 45 0.4× 71 0.7× 20 363
Daniel Ranti United States 10 124 0.9× 22 0.2× 23 0.2× 33 0.3× 19 0.2× 29 374
Rohaid Ali United States 12 100 0.7× 371 3.4× 61 0.6× 140 1.3× 13 0.1× 34 727
Hiroyuki Tatekawa Japan 17 44 0.3× 282 2.6× 54 0.5× 35 0.3× 81 0.9× 43 659
Alessandro Boaro Italy 12 101 0.7× 16 0.1× 34 0.3× 55 0.5× 165 1.7× 31 362
Sue M. Challinor United States 13 229 1.7× 11 0.1× 18 0.2× 18 0.2× 56 0.6× 17 637
Nityanand Miskin United States 9 47 0.3× 8 0.1× 91 0.8× 80 0.7× 47 0.5× 25 438
Haoting Wu China 10 16 0.1× 13 0.1× 74 0.7× 148 1.4× 28 0.3× 32 345
Ajay Hegde India 12 38 0.3× 30 0.3× 27 0.2× 176 1.6× 8 0.1× 55 375
Rachel Blue United States 8 91 0.7× 20 0.2× 20 0.2× 96 0.9× 12 0.1× 45 377

Countries citing papers authored by Martin Májovský

Since Specialization
Citations

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

Fields of papers citing papers by Martin Májovský

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Martin Májovský

This figure shows the co-authorship network connecting the top 25 collaborators of Martin Májovský. A scholar is included among the top collaborators of Martin Májovský 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 Martin Májovský. Martin Májovský 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
1.
Černý, Martin, Josef Novotný, Jan Kybic, et al.. (2025). Utility of artificial intelligence in radiosurgery for pituitary adenoma: a deep learning–based automated segmentation model and evaluation of its clinical applicability. Journal of neurosurgery. 143(2). 413–422. 1 indexed citations
2.
3.
Kybic, Jan, et al.. (2024). P.103 Automated pituitary adenoma segmentation for radiosurgery with deep learning-based model. Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques. 51(s1). S44–S44. 1 indexed citations
4.
5.
Májovský, Martin, et al.. (2024). Surgical results in patients with CNS lymphoma. Comparison of predictive value of intraoperative MRI and intraoperative histological examination for diagnostic biopsy yield. SHILAP Revista de lepidopterología. 4. 103926–103926. 1 indexed citations
6.
Májovský, Martin, et al.. (2023). Examining the Impact of Inlet Shape on Nasal Cavity Flow Field: A Computational Investigation. SHILAP Revista de lepidopterología. 383. 21–21. 1 indexed citations
7.
Májovský, Martin, et al.. (2023). Artificial Intelligence Can Generate Fraudulent but Authentic-Looking Scientific Medical Articles: Pandora’s Box Has Been Opened. Journal of Medical Internet Research. 25. e46924–e46924. 143 indexed citations
8.
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Májovský, Martin, et al.. (2022). Is nasal airflow disrupted after endoscopic skull base surgery? A short review. Neurosurgical Review. 45(6). 3641–3646. 3 indexed citations
10.
Májovský, Martin, André Grotenhuis, Nikolaos Foroglou, et al.. (2021). What is the current clinical practice in pituitary adenoma surgery in Europe? European Pituitary Adenoma Surgery Survey (EU-PASS) results—technical part. Neurosurgical Review. 45(1). 831–841. 9 indexed citations
11.
Beneš, Vladimír, Moncef Berhouma, Joham Choque‐Velasquez, et al.. (2021). Surgical treatment of symptomatic pineal cysts without hydrocephalus—meta-analysis of the published literature. Acta Neurochirurgica. 164(1). 61–77. 12 indexed citations
12.
Giammattei, Lorenzo, Daniele Starnoni, Vladimír Beneš, et al.. (2020). Extreme Lateral Supracerebellar Infratentorial Approach: Surgical Anatomy and Review of the Literature. World Neurosurgery. 147. 89–104. 16 indexed citations
13.
Májovský, Martin, et al.. (2020). Radiologická analýza výsledků implantace expandibilní náhrady při jedno- až dvou-etážové somatektomii krční páteře. 99(2). 72–76. 1 indexed citations
14.
Májovský, Martin & Vladimír Beneš. (2018). Natural course of pineal cysts—a radiographic study. Chinese Neurosurgical Journal. 4(1). 33–33. 2 indexed citations
15.
Májovský, Martin, et al.. (2018). Olfactory function in patients after transsphenoidal surgery for pituitary adenomas—a short review. Neurosurgical Review. 42(2). 395–401. 25 indexed citations
16.
Májovský, Martin, David Netuka, & Vladimír Beneš. (2017). Conservative and Surgical Treatment of Patients with Pineal Cysts: Prospective Case Series of 110 Patients. World Neurosurgery. 105. 199–205. 33 indexed citations
17.
Májovský, Martin, Alena Sumová, Lenka Pospíšilová, et al.. (2017). Melatonin and cortisol secretion profile in patients with pineal cyst before and after pineal cyst resection. Journal of Clinical Neuroscience. 39. 155–163. 24 indexed citations
18.
Masopust, Václav, David Netuka, Vladimír Beneš, et al.. (2016). Magnetic resonance imaging and histology correlation in Cushing's disease. Neurologia i Neurochirurgia Polska. 51(1). 45–52. 7 indexed citations
19.
Bělohlávek, Otakar, et al.. (2014). FLT-PET in previously untreated patients with low-grade glioma can predict their overall survival. Nuclear Medicine Review. 17(1). 7–12. 12 indexed citations
20.
Bradáč, Ondřej, Martin Májovský, Patricia de Lacy, & Vladimír Beneš. (2013). Surgery of brainstem cavernous malformations. Acta Neurochirurgica. 155(11). 2079–2083. 20 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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