Everyone Is Awesome: Analyzing Letters of Reference in a General Surgery Residency Selection Process
Bibliographic record
Abstract
BACKGROUND: The resident selection process involves the analysis of multiple data points, including letters of reference (LORs), which are inherently subjective in nature. OBJECTIVE: We assessed the frequency with which LORs use quantitative terms to describe applicants and to assess whether the use of these terms reflects the ranking of trainees in the final selection process. METHODS: A descriptive study analyzing LORs submitted by Canadian medical graduate applicants to the University of Ottawa General Surgery Program in 2019 was completed. We collected demographic information about applicants and referees and recorded the use of preidentified quantitative descriptors (eg, best, above average). A 10% audit of the data was performed. Descriptive statistics were used to analyze the demographics of our letters as well as the frequency of use of the quantitative descriptors. RESULTS: Three hundred forty-three LORs for 114 applicants were analyzed. Eighty-five percent (291 of 343) of LORs used quantitative descriptors. Eighty-four percent (95 of 113) of applicants were described as above average, and 45% (51 of 113) were described as the "best" by at least 1 letter. The candidates described as the "best" ranked anywhere from second to 108th in our ranking system. CONCLUSIONS: Most LORs use quantitative descriptors. These terms are generally positive, and while the use does discriminate between different applicants, it was not helpful in the context of ranking applicants in our file review process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".