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整形外科学标准化推荐信表格在预测住院医师匹配结果中的效用。

The Utility of the Plastic Surgery Standardized Letter of Recommendation Form in Predicting Residency Match Outcomes.

机构信息

School of Medicine, Vanderbilt University, Nashville, Tennessee.

School of Medicine, Vanderbilt University, Nashville, Tennessee.

出版信息

J Surg Educ. 2023 Jul;80(7):948-956. doi: 10.1016/j.jsurg.2023.04.012. Epub 2023 May 5.

Abstract

BACKGROUND

Letters of recommendation play an important role in resident selection. While plastic surgery's Standardized Letter of Recommendation (SLOR) form most commonly serves as an adjunct to narrative letters, the SLOR provides objective data in the review process and could eventually replace narrative letters. The utility of the SLOR in predicting Match outcomes has not been studied.

METHODS

Applicant data from 225 first-time residency applicants in 2020-21 were collected. Logistic regression modeling was used to predict Match outcomes. This model was validated using 100 randomly selected applicants from 2019-20.

RESULTS

Rank placement (SLOR Question 6) was the most important factor when predicting Match outcomes (p<0.0001). All other SLOR questions were not predictive and subject to notable score inflation. No SLOR score differences were noted based on race; female applicants were rated higher in two of ten domains (p<0.05).

CONCLUSIONS

One question on the plastic surgery SLOR was highly predictive of an applicant matching. However, the remaining SLOR questions had little utility and were subject to gross score inflation. Further work should be done to optimize the utility of the SLOR in differentiating applicants. This has important implications in ensuring the selection of professional, competent residents according to the aims of the Accreditation Council of Graduate Medical Education.

摘要

背景

推荐信在住院医师选拔中起着重要作用。虽然整形外科的标准推荐信(SLOR)表格最常作为叙述性信件的辅助工具,但 SLOR 在审查过程中提供了客观数据,并最终可能取代叙述性信件。SLOR 在预测匹配结果中的效用尚未得到研究。

方法

收集了 2020-21 年 225 名首次住院医师申请的申请人数据。使用逻辑回归模型预测匹配结果。该模型使用 2019-20 年随机选择的 100 名申请人进行了验证。

结果

排名(SLOR 问题 6)是预测匹配结果的最重要因素(p<0.0001)。其他所有 SLOR 问题都没有预测能力,并且容易出现明显的分数膨胀。种族方面没有注意到 SLOR 评分差异;女性申请人在十个领域中的两个领域的评分更高(p<0.05)。

结论

整形外科 SLOR 的一个问题高度预测了申请人的匹配。然而,其余的 SLOR 问题几乎没有用处,并且容易受到严重的分数膨胀影响。应进一步努力优化 SLOR 在区分申请人方面的效用。这对于根据研究生医学教育认证委员会的目标确保选择专业、有能力的住院医师具有重要意义。

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