Scholarlink

<p>The shift of USMLE Step 1 to pass/fail redirected residency programs’ attention to Step 2 Clinical Knowledge (CK), creating demand for accurate early‑warning tools. Develop and externally validate a concise model that predicts Step 2 CK performance midway through clerkships and guides targeted support. Data from 422 students across three cohorts were used to build a multiple linear regression (MLR) model. Predictors included National Board of Medical Examiners (NBME) subject exam scores. Predicted scores were converted to pass probabilities (≥ 214) and mapped to low‑risk (≥ 95%) and elevated‑risk (&lt; 95%) strata that triggered predefined remediation protocols. The multivariable model explained 67% of Step 2 CK score variance in-sample (R² = 0.673, <i>p</i> &lt; 0.01) and retained strong accuracy out-of-sample (predicted-vs-actual R² = 0.612). Medicine (<i>r</i> = 0.703) and Pediatrics (<i>r</i> = 0.693) NBME subject exams were the most powerful single predictors, although all six clerkship exams contributed significantly (<i>r</i> = 0.612–0.659). Residuals were normally distributed (M = 0.02 ± 7.96), indicating unbiased estimates. Risk stratification flagged 9% of students for intensified remediation; every low-risk student passed, and high-risk students who completed tutoring and structured study reached the 214 benchmarks with only one single failure, yielding a 0.9% failure rate. The model delivered actionable, early guidance for targeted support. Routinely collected NBME shelf scores can power a transparent, six‑variable model that reliably forecasts Step 2 CK outcomes months in advance. Embedding this tool within an equity‑minded remediation workflow conserves advising resources and markedly reduces failure risk.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Predictive Modeling to Support Student Success on Step 2 Clinical Knowledge: A Multi-Cohort Model

  • 期刊论文

  • 发表日期：

    2026年1月26日

查看全文
  • Phuong B. Huynh,
  • Heather E. Harrell,
  • Shelley Wells Collins,
  • Joseph C. Fantone

摘要

The shift of USMLE Step 1 to pass/fail redirected residency programs’ attention to Step 2 Clinical Knowledge (CK), creating demand for accurate early‑warning tools. Develop and externally validate a concise model that predicts Step 2 CK performance midway through clerkships and guides targeted support. Data from 422 students across three cohorts were used to build a multiple linear regression (MLR) model. Predictors included National Board of Medical Examiners (NBME) subject exam scores. Predicted scores were converted to pass probabilities (≥ 214) and mapped to low‑risk (≥ 95%) and elevated‑risk (< 95%) strata that triggered predefined remediation protocols. The multivariable model explained 67% of Step 2 CK score variance in-sample (R² = 0.673, p < 0.01) and retained strong accuracy out-of-sample (predicted-vs-actual R² = 0.612). Medicine (r = 0.703) and Pediatrics (r = 0.693) NBME subject exams were the most powerful single predictors, although all six clerkship exams contributed significantly (r = 0.612–0.659). Residuals were normally distributed (M = 0.02 ± 7.96), indicating unbiased estimates. Risk stratification flagged 9% of students for intensified remediation; every low-risk student passed, and high-risk students who completed tutoring and structured study reached the 214 benchmarks with only one single failure, yielding a 0.9% failure rate. The model delivered actionable, early guidance for targeted support. Routinely collected NBME shelf scores can power a transparent, six‑variable model that reliably forecasts Step 2 CK outcomes months in advance. Embedding this tool within an equity‑minded remediation workflow conserves advising resources and markedly reduces failure risk.