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Article
Peer-Review Record

Automated Error Labeling in Radiation Oncology via Statistical Natural Language Processing

by Indrila Ganguly 1,†, Graham Buhrman 2,†, Ed Kline 3, Seong K. Mun 4 and Srijan Sengupta 1,*
Reviewer 1:
Reviewer 2: Anonymous
Submission received: 16 January 2023 / Revised: 12 March 2023 / Accepted: 16 March 2023 / Published: 23 March 2023
(This article belongs to the Special Issue Artificial Intelligence and Radiation Oncology)

Round 1

Reviewer 1 Report

Abstract: IOM at first appearance should be expanded to Institute of Medicine

Remove citation from abstract, No citation at abstract section

You can mentioned the risk of error expected at oncology field (short sentence).

Keywords accepted in the current form.

Introduction: Please arrange the references number in this section well, (A milestone report from the Institute of Medicine (IOM), published in 2000, brought public and political attention to the severe fallout of medical errors and highlighted the need to address medical errors and their effects on human health [1]. More recently, medical 16 errors have been shown to be the third leading cause of death in the United States [5].) reference No 1 should be followed by reference No 2 not reference No 5 as appeared in the text. 

Introduction and problem statement are too long compared to the discussion section can you compromised between two sections!

Materials and Methods: This is the clear section and attractive section in the manuscript.

Results also clear to me

Discussion need to be improved.

Conclusion: try to conclude the main findings through your results as possible. 

 

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Reviewer 2 Report

see the report

Comments for author File: Comments.pdf

Author Response

Please see the attachment

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