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.
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 | 0.025 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".