Evidence map›Paper›PMID 39974318›Full record

ArticleJournal of medical education and curricular development

Impact of a USMLE Step 2 Prediction Model on Medical Student Motivations.

Anthony Shanks, Ben Steckler, Sarah Smith, Debra Rusk, Emily Walvoord, Erin Dafoe, Paul Wallach

Abstract read
In one paragraph

Article in Journal of medical education and curricular development. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Anthony ShanksWilliam H. and Sallie E. Coleman Professor of OBGYN, Vice Chair of Education and MFM Fellowship Director, Director of Career Mentoring, Indiana University School of Medicine, Indianapolis, IN, USA.ORCID https://orcid.org/0000-0001-5916-1683
Ben StecklerDirector of Business Intelligence, Indiana University School of Medicine, Indianapolis, IN, USA.
Sarah SmithBusiness Intelligence, Indiana University School of Medicine, Indianapolis, IN, USA.
Debra RuskAssistant Dean for Career Mentoring, Indiana University School of Medicine, Indianapolis, IN, USA.ORCID https://orcid.org/0000-0002-5924-3854
Emily WalvoordAssociate Dean for Student Affairs, Indiana University School of Medicine, Indianapolis, IN, USA.
Erin DafoeAssociate Director of Student Affairs, Indiana University School of Medicine, Indianapolis, IN, USA.
Paul WallachVice Chancellor for Health Sciences Education and Executive Vice Dean, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWith the transition of USMLE Step 1 to Pass/Fail, Step 2 CK carries added weight in the residency selection process. Our goal was to develop a Step 2 predicted score to provide to students earlier in medical school to assist with career mentoring. We also sought to understand how the predicted scores affected student's plans.

methodTraditional statistical models and machine learning algorithms to identify predictors of Step 2 CK performance were utilized. Predicted scores were provided to all students in the Class of 2024 at a large allopathic medical school. A cross-sectional survey was conducted to assess if the estimated score influenced career or study plans.

resultsThe independent variables that resulted in the most predictive model included CBSE score, Organ System course exam scores and Phase 2 (Third Year Clinical Clerkships) NBME percentile scores (Step2CK = 191.984 + 0.42 (CBSE score) + 0.294 (Organ Systems) + 0.409 (Average NBME). The standard error of the prediction model was 7.6 with better accuracy for predicted scores greater than 230 (SE 8.1) as compared to less than 230 (SE 12.8). Nineteen percent of respondents changed their study plan based on the predicted score result. Themes identified from the predicted score included reassurance for career planning and the creation of anxiety and stress.

conclusionA Step 2 Predicted Score, created from pre-existing metrics, was a good estimator of Step 2 CK performance. Given the timing of Step 2 CK, a predicted score would be a useful tool to counsel students during the specialty and residency selection process.

Indexed as

machine learningPredictorresidencystep 2USMLE

Identifiers

PMID39974318
PMCPMC11837067

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.