Reading Between the Lines: Determinants of a High-Quality Letter of Recommendation in Cardiothoracic Surgery
Bibliographic record
Abstract
Background: The letter of recommendation is an important component of a residency application and is useful for identifying stellar cardiothoracic surgery (CTS) candidates. We identify letter attributes and phrases that CTS program directors (PDs) find to be the most valuable in selecting candidates. Methods: An online survey was distributed to CTS PDs in the United States and Canada, querying the importance of various letter characteristics. Reponses were ranked on the basis of a weighted average. Results: Twenty-six (26.5%) CTS PDs completed the survey. Personal acquaintanceship with the writer was the most important characteristic (weighted average, 4.77). Interpersonal skills, teamwork, and professionalism were the most important applicant attributes (4.00, 3.88, 3.76). Inclusion of personal interactions or experiences with the applicant (4.65) and the phrases "I give my highest recommendation" (4.56) and "we plan to recruit this candidate" (4.52) were rated the highest. Conclusions: Describing personal interactions and experiences with the applicant, their work ethic, interpersonal skills, and teamwork is essential for a strong letter. Personal acquaintanceship with the letter writer has the greatest positive impact overall.
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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 | 0.006 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".