Predictive Value of Credit Score on Surgery Resident and Fellow Academic and Professional Performance
Bibliographic record
Abstract
Introduction Surgical specialties consistently remain among the most competitive residency and fellowship programs with some of the highest rates of unmatched applicants. Attrition in surgical specialties is as high as 30% and particularly problematic given the extended duration of training and limited number of positions. Applicants are traditionally evaluated using a streamlined set of objective metrics, such as board scores, class rank, leadership, letters of recommendation, research productivity, and volunteerism. Consumer credit scores have been shown to be predictors of personality and work performance, however, the literature has yet to explore consumer credit histories in the context of surgical resident and fellow performance. This study aims to determine whether consumer credit scores of surgery residents and fellows are predictive of academic and professional performance. Methods This is a multi-institutional observational survey study across all American Council of Graduate Medical Education and Royal College of Physicians and Surgeons accredited surgical residency and fellowship programs in the United States and Canada. Ninety-nine surgical residents and fellows with educational status of post-graduate year two or higher participated in this study. Dichotomous (yes or no) survey items were formulated to assess performance indicators in the domains of notable achievements and awards, research output, written examination performance, professionalism, and surgical/technical skills. Three-digit Fair Isaac Corporation (FICO) credit scores, a widely accepted consumer reporting score, were collected to avoid calculation variability between algorithms. Results Surgical residents and fellows reported credit scores between 611( fair) and 853 (exceptional) with a median (interquartile range) of 774 (715-833). The majority of participants 51.5%(51) reported very good credit scores. Those with higher credit scores (very good/exceptional) were 377% more likely to have one or more positive performance indicators OR (95% CI) = 3.77 (1.43-9.97). Similarly, residents with lower credit scores (fair/good) were only 40% more likely to have one or more negative performance indicators. The credit score has a moderate ability to distinguish between the presence and absence of positive performance indicators (area under the curve {AUC} = 0.70, p = 0.001). The use of 753 as a credit score cutoff is 78.9% sensitive and 52.4% specific for discerning surgery residents and fellows with one or more positive performance indicators. The credit score did not significantly discern those with negative performance indicators. Conclusions While credit score was significantly functional in discerning those with and without positive performance indicators, sensitivity and specificity rates leave much to be desired. This study suggests credit score may have a utility as a companion to traditional metrics used in identifying candidates for surgery residencies and fellowships who will have positive performance in the domains of research productivity, written examination performance, and professional awards and recognition. Additional studies are needed to assess this utility on a larger scale.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".