Neurosurgery Resident Applicant Research Evaluation: Leveling the Playing Field

Background

Neurosurgery residency is highly competitive, with applicants increasingly emphasizing research to strengthen their applications. This has led to a rise in publications but also to applicant anxiety and concerns about exaggerated contributions. To ensure fair assessment, we developed a scoring system that considers authorship position and journal impact factor for a more precise evaluation of research contributions in neurosurgery applications.

Methods

A quality-adjusted research index was developed using authorship position and journal impact factor. Peer-reviewed, PubMed-indexed publications were analyzed for 303 applicants to our residency program. Demographic data, including sex, medical school ranking, IMG status, and advanced degrees, were collected. Research index scores were compared between applicants invited to interview (Group A) and those not invited (Group B). Receiver operating characteristic (ROC) curve analysis determined the index’s predictive accuracy for interview invitations.

Results

Group A had significantly higher research index scores (78.2 [48.1-133.5] vs. 37.0 [7.8-92.3], p < 0.01) and publication counts (10 [7-15] vs. 6 [2-12], p < 0.01) compared to Group B. First and second authorships were more prevalent among Group A (23.6% vs. 15.0%, p = 0.049 and 23.6% vs. 14.3%, p < 0.01, respectively). Male sex, attendance at highly ranked medical schools, and MD/PhD status were significant predictors of higher research index scores. A cutoff score of ≥41.2 yielded 87% sensitivity and 53% specificity in predicting interview invitations, with an AUC of 69.7%.

Conclusion

The proposed research index objectively evaluates the quality and significance of neurosurgery residency applicants’ scholarly contributions, emphasizing meaningful research engagement. By integrating journal impact factors and authorship roles, the system reduces bias from inflated publication counts and misrepresented research claims, fostering transparency and fairness in applicant evaluation. Future efforts should focus on automating this tool for broader applicability across medical specialties.