The Value of Signaling an Orthopaedic Surgery Program: A Survey to Orthopaedic Surgery Programs
Bibliographic record
Abstract
INTRODUCTION: A new system was implemented by the Association of American Medical Colleges called the preference signaling program for the 2022 to 2023 orthopaedic surgery residency match. Applicants were able to signal 30 orthopaedic surgery programs to indicate high interest in a specific program. The purpose of this study was to address how important signaling was to an orthopaedic surgery program this 2022 to 2023 application cycle. METHODS: A five-question survey was sent to orthopaedic surgery residency programs participating in the Electronic Residency Application Service this application cycle. Contact information was gathered through the Accreditation Council for Graduate Medical Education residency website and program websites. RESULTS: Responses were obtained from 69 of the 151 programs (46%) contacted. The average number of applicants per program was 727 (range, 372 to 1031, SD 155). Thirty-four of 61 respondents (56%) stated that 100% of their interviewees signaled their program. Fifty-five of 61 respondents (90%) indicated that their interviewee pool consisted of 75% or more applicants who signaled. Applicants who signaled had a 24.4% (range, 12.77 to 47.41, SD 8.04) chance of receiving an interview. Applicants who did not signal had just a 0.92% (range, 0 to 13.10, SD 2.08) chance of receiving an interview. Fifty-four of the 63 applicants (86%) answered that signaling played an important role in considering an applicant for an interview. CONCLUSION: Over half of the responding programs only interviewed applicants who signaled their program, and over 90% of programs' interview lists consisted of at least 75% of signaling applicants. Eighty-six percent of programs indicated that signaling played an important role in considering an applicant for an interview. Applicants who signaled were 26.5 times more likely to receive an interview than those who did not (P < 0.0001). With this information, applicants can narrow down their list of programs to apply to, knowing that their signal to a program will give them a better chance at receiving an interview.
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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 | 0.072 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".