Suppr超能文献

超越放疗匹配中的“图表结果”:对自我报告的申请人数据的分析。

Beyond 'charting outcomes' in the radiation oncology match: analysis of self-reported applicant data.

机构信息

a Department of Human Oncology , University of Wisconsin School of Medicine and Public Health , Madison , WI , USA.

b Department of Radiation Oncology , H. Lee Moffitt Cancer Center and Research Institute , Tampa , FL , USA.

出版信息

Med Educ Online. 2018 Dec;23(1):1489691. doi: 10.1080/10872981.2018.1489691.

Abstract

The Charting Outcomes resource is useful in gauging an applicant's competiveness for a given specialty. However, many variables are not reported in Charting Outcomes that may influence an applicant's ability to match. A significant proportion of applicants record their experiences in an anonymous, self-reported applicant spreadsheet. We analyzed factors associated with a successful match using this dataset to test the hypothesis that research productivity and high academic performance correlates with success rates. A retrospective analysis of "RadOnc Interview Spreadsheet" for the 2015, 2016, and 2017 radiation oncology match was performed. Data were accessed via studentdoctor.net. Board scores, research characteristics, Sub-I participation, and interview invitation rates were available. Mann-Whitney U, Kruskal-Wallis, and chi-square tests were used for statistical analysis. When possible, results were compared to those reported in the National Residency Match Program's "Charting Outcomes" report. A total of 158 applicants were examined for the applicant characteristics. Applicants applied to a median of 61 programs and received a median of 14 interviews. The mean step 1 score was 248 (range: 198 to 272) and most were in the highest grade point average quartile (68.3%). 21.7% participated in additional research year(s), and 19% obtained a PhD. The majority of applicants took three radiation oncology electives (48.7%). On multivariate analysis, alpha-omega-alpha (AOA) honors society status (p=0.033), participating in a research year (p=0.001) and number of journal publications (p=0.047) significantly correlated with higher interview invitation rates. In summary, this study identifies important considerations for radiation oncology applicants that have not been previously reported, such as induction into AOA and number of journal publications.

摘要

图表结果资源可用于评估申请人在特定专业中的竞争力。然而,图表结果中并未报告许多可能影响申请人匹配能力的变量。很大一部分申请人在匿名的自我报告的申请人电子表格中记录他们的经历。我们使用此数据集分析与成功匹配相关的因素,以检验研究生产力和高学业成绩与成功率相关的假设。对 2015 年、2016 年和 2017 年放射肿瘤学匹配的“RadOnc 面试电子表格”进行了回顾性分析。通过 studentdoctor.net 访问数据。可获得成绩、研究特征、次实习参与和面试邀请率。使用 Mann-Whitney U、Kruskal-Wallis 和卡方检验进行统计分析。在可能的情况下,将结果与全国住院医师匹配计划的“图表结果”报告中的结果进行比较。共检查了 158 名申请人的申请人特征。申请人平均申请了 61 个项目,平均收到 14 个面试邀请。第 1 步的平均分数为 248(范围:198 至 272),大多数申请人在最高平均绩点四分位数(68.3%)中。21.7%的人参加了额外的研究年,19%的人获得了博士学位。大多数申请人参加了三门放射肿瘤学选修课程(48.7%)。多元分析显示,阿尔法欧米伽阿尔法(AOA)荣誉学会身份(p=0.033)、参加研究年(p=0.001)和期刊出版物数量(p=0.047)与更高的面试邀请率显著相关。总之,这项研究确定了放射肿瘤学申请人以前未报告的重要考虑因素,例如 AOA 入会和期刊出版物数量。

相似文献

1
Beyond 'charting outcomes' in the radiation oncology match: analysis of self-reported applicant data.
Med Educ Online. 2018 Dec;23(1):1489691. doi: 10.1080/10872981.2018.1489691.
2
Comparison of Self-Reported Data on Student Doctor Network to Objective Data of the National Resident Matching Program.
J Am Coll Radiol. 2017 Dec;14(12):1594-1597. doi: 10.1016/j.jacr.2017.08.011. Epub 2017 Oct 31.
4
Analysis of the 1990-2007 neurosurgery residency match: does applicant gender affect neurosurgery match outcome?
J Neurosurg. 2018 Aug;129(2):282-289. doi: 10.3171/2017.11.JNS171831. Epub 2018 Jun 8.
6
7
The Orthopaedic Surgery Residency Application Process: An Analysis of the Applicant Experience.
J Am Acad Orthop Surg. 2018 Aug 1;26(15):537-544. doi: 10.5435/JAAOS-D-16-00835.
8
Evaluation of the National Resident Matching Program (NRMP) radiation oncology data (1993-2003).
Int J Radiat Oncol Biol Phys. 2003 Nov 15;57(4):1033-7. doi: 10.1016/s0360-3016(03)00734-x.
9
Applicant Interview Experiences and Postinterview Communication of the 2016 Radiation Oncology Match Cycle.
Int J Radiat Oncol Biol Phys. 2016 Nov 1;96(3):514-20. doi: 10.1016/j.ijrobp.2016.08.009. Epub 2016 Aug 21.
10
Trends in Radiation Oncology Residency Applicant Interview Experiences and Post-Interview Communication.
Int J Radiat Oncol Biol Phys. 2019 Mar 15;103(4):818-822. doi: 10.1016/j.ijrobp.2018.11.042. Epub 2018 Nov 27.

引用本文的文献

2
Current Trends of Research Productivity among Students Matching at Top Ophthalmology Programs.
J Acad Ophthalmol (2017). 2022 May 24;14(1):e133-e140. doi: 10.1055/s-0042-1746423. eCollection 2022 Jan.
3
Characteristics of First-Year Residents in Top-Ranked United States Ophthalmology Residency Programs.
J Acad Ophthalmol (2017). 2022 Jan 30;14(1):e7-e17. doi: 10.1055/s-0041-1735152. eCollection 2022 Jan.
4
Correlation Between Research Productivity During Medical School and Radiation Oncology Residency.
Adv Radiat Oncol. 2023 Mar 15;8(4):101219. doi: 10.1016/j.adro.2023.101219. eCollection 2023 Jul-Aug.
5
Potential Implications of the New USMLE Step 1 Pass/Fail Format for Diversity Within Radiation Oncology.
Adv Radiat Oncol. 2020 Jul 18;6(1):100524. doi: 10.1016/j.adro.2020.07.001. eCollection 2021 Jan-Feb.
7
Residency Interviews in Radiation Oncology After COVID-19: Perspectives From Recently Matched Applicants.
Int J Radiat Oncol Biol Phys. 2020 Oct 1;108(2):452-454. doi: 10.1016/j.ijrobp.2020.05.040.
8
Mind the Gap: An Analysis of "Gap Year" Prevalence, Productivity, and Perspectives Among Radiation Oncology Residency Applicants.
Int J Radiat Oncol Biol Phys. 2019 Jun 1;104(2):456-462. doi: 10.1016/j.ijrobp.2019.02.006. Epub 2019 Feb 11.

本文引用的文献

1
Comparison of Self-Reported Data on Student Doctor Network to Objective Data of the National Resident Matching Program.
J Am Coll Radiol. 2017 Dec;14(12):1594-1597. doi: 10.1016/j.jacr.2017.08.011. Epub 2017 Oct 31.
2
Attracting Future Radiation Oncologists: An Analysis of the National Resident Matching Program Data Trends From 2004 to 2015.
Int J Radiat Oncol Biol Phys. 2015 Dec 1;93(5):965-7. doi: 10.1016/j.ijrobp.2015.08.020. Epub 2015 Oct 8.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验