Ah‐Young Oh

2.7k total citations
123 papers, 1.8k citations indexed

About

Ah‐Young Oh is a scholar working on Surgery, Anesthesiology and Pain Medicine and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Ah‐Young Oh has authored 123 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Surgery, 63 papers in Anesthesiology and Pain Medicine and 36 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Ah‐Young Oh's work include Anesthesia and Sedative Agents (41 papers), Anesthesia and Pain Management (36 papers) and Cardiac, Anesthesia and Surgical Outcomes (32 papers). Ah‐Young Oh is often cited by papers focused on Anesthesia and Sedative Agents (41 papers), Anesthesia and Pain Management (36 papers) and Cardiac, Anesthesia and Surgical Outcomes (32 papers). Ah‐Young Oh collaborates with scholars based in South Korea, Ethiopia and United States. Ah‐Young Oh's co-authors include Jung‐Hee Ryu, Jung‐Won Hwang, Young-Tae Jeon, Kwang‐Suk Seo, Sung‐Hee Han, Bon‐Wook Koo, Hee‐Pyoung Park, Sang-Hwan Do, In‐Ae Song and Jin‐Woo Park and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Cancer Research.

In The Last Decade

Ah‐Young Oh

114 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ah‐Young Oh South Korea 23 789 752 379 266 222 123 1.8k
Jung‐Hee Ryu South Korea 26 1.0k 1.3× 813 1.1× 420 1.1× 244 0.9× 206 0.9× 131 2.1k
Karine Nouette‐Gaulain France 23 788 1.0× 369 0.5× 380 1.0× 164 0.6× 279 1.3× 93 1.5k
George Silvay United States 16 584 0.7× 351 0.5× 581 1.5× 256 1.0× 158 0.7× 111 1.7k
Norah N. Naughton United States 21 973 1.2× 603 0.8× 387 1.0× 94 0.4× 164 0.7× 46 1.9k
Ehab Farag United States 22 653 0.8× 462 0.6× 401 1.1× 138 0.5× 89 0.4× 72 1.4k
Kimitoshi Nishiwaki Japan 18 575 0.7× 256 0.3× 362 1.0× 154 0.6× 91 0.4× 128 1.1k
Elizabeth Hughes United Kingdom 21 262 0.3× 454 0.6× 510 1.3× 131 0.5× 161 0.7× 45 1.8k
Mary Cooter United States 21 467 0.6× 324 0.4× 429 1.1× 112 0.4× 55 0.2× 82 1.4k
Vafi Salmasi United States 17 753 1.0× 217 0.3× 592 1.6× 188 0.7× 103 0.5× 41 1.6k
Christian Jayr France 26 763 1.0× 362 0.5× 330 0.9× 694 2.6× 426 1.9× 50 2.0k

Countries citing papers authored by Ah‐Young Oh

Since Specialization
Citations

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

Fields of papers citing papers by Ah‐Young Oh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ah‐Young Oh. 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 Ah‐Young Oh. The network helps show where Ah‐Young Oh may publish in the future.

Co-authorship network of co-authors of Ah‐Young Oh

This figure shows the co-authorship network connecting the top 25 collaborators of Ah‐Young Oh. A scholar is included among the top collaborators of Ah‐Young Oh 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 Ah‐Young Oh. Ah‐Young Oh 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.
Park, Insun, Jae Hyon Park, Chang‐Hoon Koo, et al.. (2024). Assessment of machine learning classifiers for predicting intraoperative blood transfusion in non-cardiac surgery. Transfusion Clinique et Biologique. 32(1). 1–8. 1 indexed citations
2.
3.
Koo, Chang‐Hoon, et al.. (2024). Is quantitative neuromuscular monitoring mandatory after administration of the recommended dose of sugammadex? A prospective observational study. Anaesthesia Critical Care & Pain Medicine. 43(6). 101445–101445. 1 indexed citations
4.
Park, Insun, Jae Hyon Park, Chang‐Hoon Koo, et al.. (2024). Feasibility of a Machine Learning Classifier for Predicting Post-Induction Hypotension in Non-Cardiac Surgery. Yonsei Medical Journal. 66(3). 160–160.
5.
Koo, Bon‐Wook, Ah‐Young Oh, Hyo‐Seok Na, Jiwon Han, & Hyun Jung Kim. (2024). Goal-directed fluid therapy on the postoperative complications of laparoscopic hepatobiliary or pancreatic surgery: An interventional comparative study. PLoS ONE. 19(12). e0315205–e0315205.
6.
Park, Insun, Jae Hyon Park, Hyo‐Seok Na, et al.. (2024). Machine learning model of facial expression outperforms models using analgesia nociception index and vital signs to predict postoperative pain intensity: a pilot study. Korean journal of anesthesiology. 77(2). 195–204. 4 indexed citations
7.
Park, Insun, Jae Hyon Park, Bonwook Koo, et al.. (2024). Predicting stroke volume variation using central venous pressure waveform: a deep learning approach. Physiological Measurement. 45(9). 95007–95007.
8.
9.
Park, Insun, Jae Hyon Park, In‐Ae Song, et al.. (2023). Artificial intelligence model predicting postoperative pain using facial expressions: a pilot study. Journal of Clinical Monitoring and Computing. 38(2). 261–270. 4 indexed citations
10.
Han, Jiwon, et al.. (2021). Effects of Anesthetic Technique on the Occurrence of Acute Kidney Injury after Spine Surgery: A Retrospective Cohort Study. Journal of Clinical Medicine. 10(23). 5653–5653. 8 indexed citations
11.
12.
Kang, So‐mi, Su Jin Lee, Ah‐Young Oh, et al.. (2019). p53 induces senescence through Lamin A/C stabilization-mediated nuclear deformation. Cell Death and Disease. 10(2). 107–107. 36 indexed citations
13.
Cho, Jung-Hyun, Ah‐Young Oh, So‐Young Park, et al.. (2018). Loss of NF2 Induces TGFβ Receptor 1–mediated Noncanonical and Oncogenic TGFβ Signaling: Implication of the Therapeutic Effect of TGFβ Receptor 1 Inhibitor on NF2 Syndrome. Molecular Cancer Therapeutics. 17(11). 2271–2284. 15 indexed citations
14.
Park, So‐Young, Ah‐Young Oh, Jung-Hyun Cho, et al.. (2018). Therapeutic Effect of Quinacrine, an Antiprotozoan Drug, by Selective Suppression of p-CHK1/2 in p53-Negative Malignant Cancers. Molecular Cancer Research. 16(6). 935–946. 13 indexed citations
15.
Yu, Hyeong Won, In Hwa Bae, Su‐jin Kim, et al.. (2018). Comparison of Intra-Operative Vital Sign Changes during Total Thyroidectomy in Patients with Controlled and Uncontrolled Graves’ Disease. Journal of Clinical Medicine. 7(12). 566–566. 3 indexed citations
16.
Oh, Ah‐Young, Jiseon Kim, Jeehyun Lee, et al.. (2016). Inhibiting DX2-p14/ARF Interaction Exerts Antitumor Effects in Lung Cancer and Delays Tumor Progression. Cancer Research. 76(16). 4791–4804. 31 indexed citations
17.
Ryu, Jung‐Hee, Jung‐Won Hwang, Jeong‐Hwa Seo, et al.. (2013). Efficacy of butylscopolamine for the treatment of catheter-related bladder discomfort: a prospective, randomized, placebo-controlled, double-blind study. British Journal of Anaesthesia. 111(6). 932–937. 50 indexed citations
18.
Hwang, Jung‐Won, et al.. (2012). Drug‐administration sequence of target‐controlled propofol and remifentanil influences the onset of rocuronium. A double‐blind, randomized trial. Acta Anaesthesiologica Scandinavica. 56(5). 558–564. 11 indexed citations
19.
Oh, Ah‐Young, et al.. (2011). Isoflurane decreases death of human embryonic stem cell-derived, transcriptional marker Nkx2.5+ cardiac progenitor cells. Acta Anaesthesiologica Scandinavica. 55(9). 1124–1131. 13 indexed citations
20.
Oh, Ah‐Young, et al.. (2007). Comparison of desflurane with sevoflurane for the incidence of oculocardiac reflex in children undergoing strabismus surgery. British Journal of Anaesthesia. 99(2). 262–265. 22 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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