Tipping the Scales: Quantifying the Impact of USMLE Step 1 Pass/Fail Scoring on Application Interpretation in the Integrated Plastic Surgery Match.

OBJECTIVE

The United States Medical Licensing Exam® (USMLE®) Step 1 exam implemented pass/fail scoring to "create a more holistic residency application selection process." We aimed to assess whether this transition favored holistic review or redirected emphasis to other metrics.

DESIGN

Quantifiable data from 330 Plastic Surgery Common Applications (PSCAs) submitted to a single academic center were collected. Applicants were stratified into 2 groups based on numeric or pass/fail Step 1 score. Logistic regression was performed for each group to assess the odds of matching based on quantifiable application components.

SETTING

2023-2024 Match cycle.

PARTICIPANTS

330 integrated plastic surgery applicants.

RESULTS

A total of 137 applications reported numeric Step 1 scores, while 193 reported pass/fail scores. For applicants with a numeric Step 1 score, the factors most predictive of matching were Step 2 score >265 (OR 159 [95% CI, 6.6-999], Step 1 Score between 240 and 250 (OR 21.8 [95% CI, 4.8-98.6]), and being a U.S. citizen (OR 21.1 [95% CI, 3.9-116]). Letter of recommendation numeric strength (OR 11.0 [95% CI, 2.7-45.6]) was also a significant match predictor, followed by more than 16 podium presentations (OR 10.8 [95% CI, 2.6-45.5]). In comparison, for students with pass/fail Step 1 scores, there were only 3 significant factors that predicted a successful match: strength of recommendation letters (OR 3.7 [95% CI, 1.8-7.6]), Alpha Omega Alpha society membership (OR 3.2 [95% CI, 1.3-7.7]), and graduation from a U.S News and World Report top 40 medical school in research (OR 3.0 [95% CI, 1.5-6.1]).

CONCLUSION

For applicants with a numeric Step 1 score, Step 1 and 2 scores remained the most critical match factors. In comparison, letter of recommendation strength and graduation from a top 40 medical school were most important for applicants with pass/fail scoring, indicating a shift toward other application metrics following the scoring change.