The standardized letter of evaluation (SLOE) in emergency medicine: The internal validity of the SLOE 2.0
Bibliographic record
Abstract
Background: The standardized letter of evaluation (SLOE) is a crucial component of emergency medicine (EM) residency applications. Initially developed in 1995 and revised to electronic SLOE (eSLOE) 2.0, this tool aims to provide a standardized evaluation of medical students. Objective: This study aimed to conduct internal validation by analyzing the distribution and correlation of scores in eSLOE 2.0 and identify any ranking skew. Methods: A multi-institutional cross-sectional study conducted using eSLOE 2.0 data from applicants to five geographically diverse U.S. EM residency programs during the 2022-2023 application cycle. Data from 2891 eSLOE 2.0 s across 1633 applicants were analyzed using descriptive statistics, chi-square, and Spearman's rho. Results: Scores for all questions were moderately left-skewed. The mean scores for all part B questions were above 4.0. Strong correlations were found between part A and B scores with anticipated guidance (AG) and rank list (RL) positions. The AG had a higher correlation with RL positions than grades. The mean RL score indicated that the average student fell between the middle and top thirds. Conclusions: The study demonstrates left skew in eSLOE 2.0 scoring, including a higher prevalence of scores in the fully and mostly entrustable ranges for part A and the consistently high scores in part B.
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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 | 0.036 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".