Akash Shah

3.4k total citations
71 papers, 814 citations indexed

About

Akash Shah is a scholar working on Surgery, Pathology and Forensic Medicine and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Akash Shah has authored 71 papers receiving a total of 814 indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Surgery, 16 papers in Pathology and Forensic Medicine and 11 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Akash Shah's work include Spine and Intervertebral Disc Pathology (16 papers), Infectious Diseases and Tuberculosis (12 papers) and Amoebic Infections and Treatments (8 papers). Akash Shah is often cited by papers focused on Spine and Intervertebral Disc Pathology (16 papers), Infectious Diseases and Tuberculosis (12 papers) and Amoebic Infections and Treatments (8 papers). Akash Shah collaborates with scholars based in United States, India and United Kingdom. Akash Shah's co-authors include Joseph H. Schwab, Mitchel B. Harris, Aditya V. Karhade, Paul T. Ogink, Sandra B. Nelson, Christopher M. Bono, Joseph H. Schwab, Sai K. Devana, Nelson F. SooHoo and Mihaela van der Schaar and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Bone and Joint Surgery and Spine.

In The Last Decade

Akash Shah

59 papers receiving 807 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Akash Shah United States 17 576 194 142 141 113 71 814
Thomas Jack Germany 18 248 0.4× 150 0.8× 40 0.3× 155 1.1× 43 0.4× 51 986
Awais Ashfaq United States 12 246 0.4× 37 0.2× 15 0.1× 41 0.3× 70 0.6× 50 613
Tobias Gauss France 16 317 0.6× 22 0.1× 22 0.2× 26 0.2× 67 0.6× 66 921
Ronit Brodie Israel 8 156 0.3× 17 0.1× 18 0.1× 61 0.4× 56 0.5× 21 674
Kazutoshi Fujibayashi Japan 15 365 0.6× 117 0.6× 40 0.3× 7 0.0× 58 0.5× 53 880
Devyani Chowdhury United States 16 338 0.6× 19 0.1× 56 0.4× 121 0.9× 76 0.7× 63 901
Ji Hoon Kim South Korea 16 84 0.1× 16 0.1× 40 0.3× 42 0.3× 34 0.3× 71 723
Shail M. Govani United States 18 279 0.5× 42 0.2× 62 0.4× 10 0.1× 102 0.9× 59 985
Pattharawin Pattharanitima Thailand 14 130 0.2× 20 0.1× 36 0.3× 13 0.1× 69 0.6× 57 607
Kuang-Yi Chang Taiwan 13 303 0.5× 53 0.3× 56 0.4× 18 0.1× 55 0.5× 39 675

Countries citing papers authored by Akash Shah

Since Specialization
Citations

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

Fields of papers citing papers by Akash Shah

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Akash Shah

This figure shows the co-authorship network connecting the top 25 collaborators of Akash Shah. A scholar is included among the top collaborators of Akash Shah 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 Akash Shah. Akash Shah 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.
Shah, Akash, et al.. (2024). An Analysis of the Cost Variation Among Different Antimicrobial Agents: The Indian Scenario. Cureus. 16(7). e64538–e64538. 1 indexed citations
2.
Shah, Akash, et al.. (2024). Psilocybin-assisted psychotherapy for existential distress: practical considerations for therapeutic application—a review. Annals of Palliative Medicine. 13(6). 1490–1501. 3 indexed citations
3.
Upfill‐Brown, Alexander, et al.. (2024). Does the use of tranexamic acid intraoperatively reduce postoperative blood loss and complications following biportal endoscopic lumbosacral decompression?. Journal of Spine Surgery. 10(1). 68–79. 4 indexed citations
4.
Shah, Akash, Aditya V. Karhade, Olivier Q. Groot, et al.. (2023). External validation of a predictive algorithm for in-hospital and 90-day mortality after spinal epidural abscess. The Spine Journal. 23(5). 760–765. 3 indexed citations
5.
6.
Sheppard, William L., et al.. (2023). Spondylolisthesis and mismatch deformity affect outcomes after total knee arthroplasty. Journal of Orthopaedic Surgery and Research. 18(1). 157–157. 2 indexed citations
7.
Chandran, Nishanth, Divya Gupta, & Akash Shah. (2022). Circuit-PSI With Linear Complexity via Relaxed Batch OPPRF. SHILAP Revista de lepidopterología. 30 indexed citations
8.
Karhade, Aditya V., Olivier Q. Groot, Akash Shah, et al.. (2022). Development and external validation of predictive algorithms for six-week mortality in spinal metastasis using 4,304 patients from five institutions. The Spine Journal. 22(12). 2033–2041. 16 indexed citations
9.
10.
Shah, Akash, Sai K. Devana, Changhee Lee, et al.. (2022). A Risk Calculator for the Prediction of C5 Nerve Root Palsy After Instrumented Cervical Fusion. World Neurosurgery. 166. e703–e710. 6 indexed citations
11.
Upfill‐Brown, Alexander, Beau P. Sperry, Akash Shah, et al.. (2022). National trends in the utilization of lumbar disc replacement for lumbar degenerative disc disease over a 10-year period, 2010 to 2019. Journal of Spine Surgery. 8(3). 343–352. 6 indexed citations
12.
Devana, Sai K., Akash Shah, Changhee Lee, et al.. (2021). Development of a Machine Learning Algorithm for Prediction of Complications and Unplanned Readmission Following Reverse Total Shoulder Arthroplasty. SHILAP Revista de lepidopterología. 5. 4121958364–4121958364. 16 indexed citations
13.
Shah, Akash, Sai K. Devana, Chang‐Hee Lee, et al.. (2021). Prediction of Major Complications and Readmission After Lumbar Spinal Fusion: A Machine Learning–Driven Approach. World Neurosurgery. 152. e227–e234. 23 indexed citations
14.
Devana, Sai K., et al.. (2021). A Novel, Potentially Universal Machine Learning Algorithm to Predict Complications in Total Knee Arthroplasty. Arthroplasty Today. 10. 135–143. 23 indexed citations
15.
Shah, Akash, Aditya V. Karhade, Howard Y. Park, et al.. (2021). Updated external validation of the SORG machine learning algorithms for prediction of ninety-day and one-year mortality after surgery for spinal metastasis. The Spine Journal. 21(10). 1679–1686. 29 indexed citations
16.
Shah, Akash, et al.. (2020). Development of a Novel, Potentially Universal Machine Learning Algorithm for Prediction of Complications After Total Hip Arthroplasty. The Journal of Arthroplasty. 36(5). 1655–1662.e1. 30 indexed citations
17.
Karhade, Aditya V., Akash Shah, Christopher M. Bono, et al.. (2019). Development of machine learning algorithms for prediction of mortality in spinal epidural abscess. The Spine Journal. 19(12). 1950–1959. 48 indexed citations
18.
Karhade, Aditya V., et al.. (2019). Neutrophil to lymphocyte ratio and mortality in spinal epidural abscess. The Spine Journal. 19(7). 1180–1185. 18 indexed citations
19.
Yang, Huiliang, Akash Shah, Sandra B. Nelson, & Joseph H. Schwab. (2018). Fungal spinal epidural abscess: a case series of nine patients. The Spine Journal. 19(3). 516–522. 15 indexed citations
20.
Karhade, Aditya V., et al.. (2018). Albumin and Spinal Epidural Abscess: Derivation and Validation in Two Independent Data Sets. World Neurosurgery. 123. e416–e426. 13 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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