Evidence map›Paper›PMID 40574935›Full record

ArticleAEM education and training2025

Program Signaling in Emergency Medicine: Applicant Trends and Outcomes From the 2023 and 2024 Match.

Andrew D Luo, Christopher Zeuthen, Elizabeth Barrall Werley, Eric Shappell, Alexis Pelletier-Bui, Molly Estes, Megan Fix, Carl Preiksaitis, Angela P Mihalic, Daniel J Egan

Abstract read
In one paragraph

Article in AEM education and training, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Andrew D LuoDepartment of Emergency Medicine, Brigham and Women's Hospital Harvard Medical School Boston Massachusetts USA.ORCID https://orcid.org/0000-0002-8367-7185
Christopher ZeuthenDepartment of Emergency Medicine, Massachusetts General Hospital Harvard Medical School Boston Massachusetts USA.
Elizabeth Barrall WerleyDepartment of Emergency Medicine Penn State College of Medicine/Penn State Health Milton S. Hershey Medical Center Hershey Pennsylvania USA.ORCID https://orcid.org/0000-0002-1493-3173
Eric ShappellDepartment of Emergency Medicine, Massachusetts General Hospital Harvard Medical School Boston Massachusetts USA.ORCID https://orcid.org/0000-0003-0281-3219
Alexis Pelletier-BuiDepartment of Emergency Medicine Cooper University Hospital/Cooper Medical School of Rowan University Camden New Jersey USA.ORCID https://orcid.org/0000-0002-9969-4123
Molly EstesDepartment of Emergency Medicine Northwestern University Chicago Illinois USA.ORCID https://orcid.org/0000-0002-4806-2986
Megan FixDepartment of Emergency Medicine University of Utah School of Medicine Salt Lake City Utah USA.
Carl PreiksaitisDepartment of Emergency Medicine Stanford School of Medicine Stanford California USA.ORCID https://orcid.org/0000-0002-3856-0068
Angela P MihalicDepartment of Pediatrics University of Texas Southwestern Medical Center Dallas Texas USA.
Daniel J EganDepartment of Emergency Medicine, Brigham and Women's Hospital Harvard Medical School Boston Massachusetts USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Program signals were introduced to the emergency medicine (EM) residency application process during the 2022-2023 and 2023-2024 application cycles, allowing applicants to express interest in specific programs. Despite widespread adoption, the relationship between signal usage and applicant outcomes remains poorly understood. This study evaluates patterns of signal utilization and their association with interview offers and match outcomes during the initial implementation in EM. Methods: We conducted a retrospective analysis of the Texas Seeking Transparency in Application to Residency (Texas STAR) database, examining US allopathic and osteopathic senior medical students applying to EM residency programs during two application cycles (2022-2023 and 2023-2024). We analyzed program signal (PS) distribution patterns using Results: The study included 967 EM applicants across two application cycles (478 in 2022-2023, 489 in 2023-2024), who sent 1919 signals in 2022-2023 and 3170 in 2023-2024. Signal distribution was highly concentrated, with the top 10% of programs receiving 35% of all signals in both application cycles. Interview yield was higher at signaled programs (2023 cycle: 76.3%, 2024 cycle: 78.9%) compared to programs overall (2023 cycle: 51.3%, 2024 cycle: 43.5%). In logistic regression analysis, sending a program signal was associated with increased odds of receiving an interview offer (2023 cycle: OR 4.40, 95% CI 3.90-4.92; 2024 cycle: OR 3.79, 95% CI 3.42-4.14), and matching after interviewing (2023 cycle: OR 5.13, 95% CI 4.08-6.47; 2024 cycle: OR 4.94, 95% CI 3.98-6.15). Conclusion: Program signals are associated with improved odds of receiving interviews and matching at EM programs. Future studies should investigate how signals affect the likelihood of receiving interview offers for applicants across different levels of competitiveness.

Indexed as

emergency medicineERASmatchmedical educationprogram signalingTexas STAR

Identifiers

PMID40574935
PMCPMC12189756

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.