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.