The Power of Preference Signaling: A Monumental Shift in the Orthopaedic Surgery Application Process
Bibliographic record
Abstract
INTRODUCTION: Orthopaedic surgery has consistently been one of the most competitive specialties in the US residency selection process. This is due in part to the steady upward trend in average applications received per program and average applications submitted per applicant, which is of growing concern. With the implementation of the Preference Signaling Program, the total number of applications has now dropped for the first time in many years, indicating signaling may improve the application process. The hypothesis is that signaling has led to a decrease in applications sent by applicants and a decrease in applications received by programs. METHODS: A 7-question survey regarding their interview and match statistics was sent to orthopaedic surgery residency programs that participated in the Electronic Residency Application Service during the 2023-2024 application cycle. A response from the program director/administrator was then recorded. RESULTS: Our program search yielded 159 programs with 106 respondents (66.7%). 82 programs (78.8%) solely interviewed applicants who signaled their program. 92.7% of current interns signaled the program where they matched, and 88 programs (84.6%) matched only applicants who signaled. 95 programs (89.6%) revealed that implementing signaling has improved the application process. CONCLUSION: Most of the programs only interviewed applicants who also signaled, and nearly all matched orthopaedic surgery applicants from the 2022-2023 cycle signaled their matching program. Orthopaedic surgery applicants should consider only applying to 30 programs and using all 30 available signals. Applicants should also be more confident knowing that beyond the 30 signals they use, there is limited support to say that they will receive an interview outside of these 30 applications. Orthopaedic surgery programs will also now have the ability to allocate more time to applicants most interested in their program, given the reduction of applications.
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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 | 0.047 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".