Evidence map›Paper›PMID 39429415›Full record

ReviewCureus2024

Program Signaling and Geographic Preferences in the United States Residency Match for Neurosurgery.

Ahmad Ozair, Jacob T Hanson, Donald K Detchou, Matthew P Blackwell, Abigail Jenkins, Marianne I Tissot, Umaru Barrie, Michael W McDermott

Abstract readReview
In one paragraph

Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

8 authors.

Ahmad OzairDepartment of Neurosurgery, University of Maryland School of Medicine, Baltimore, USA.
Jacob T HansonDepartment of Neurosurgery, Rocky Vista University College of Osteopathic Medicine, Parker, USA.
Donald K DetchouDepartment of Neurosurgery, University of Pennsylvania Perelman School of Medicine, Pennsylvania, USA.
Matthew P BlackwellDepartment of Neurosurgery, Indiana University School of Medicine, Indianapolis, USA.
Abigail JenkinsDepartment of Neurosurgery, University of Texas Southwestern Medical School, Dallas, USA.
Marianne I TissotDepartment of Neurosurgery, University of Pennsylvania Perelman School of Medicine, Pennsylvania, USA.
Umaru BarrieDepartment of Neurosurgery, University of Texas Southwestern Medical School, Dallas, USA.
Michael W McDermottDepartment of Neurosurgery, Miami Neuroscience Institute, Baptist Health South Florida, Miami, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Postgraduate residency training has long been the cornerstone of academic medicine in the United States. The Electronic Residency Application Service (ERAS), managed by the Association of American Medical Colleges (AAMC), is the central residency application platform in the United States for most clinical specialties, with the National Residency Matching Program (NRMP) being the algorithm for matching residency programs with applicants. However, the determination of the best fit between ERAS applicants and programs has been increasingly challenged by the rising number of applicants per residency spot. This application overburdening across competitive specialties led to several adverse downstream effects, which affected all stakeholders. While several changes and proposals were made to rectify the issue of application overburdening, the 2020-2021 ERAS Match Cycle finally saw several competitive specialties, including otolaryngology and urology, utilize a new system of supplemental residency application based on preference signals/tokens. These tokens permit applicants to electronically signal a select number of programs in a specialty of choice, with the program reviewing the application now cognizant that they have been signaled, i.e., the applicant has chosen to use up a limited set of signals for their program. Initial results from otolaryngology and urology, as described in this article, indicated the value of this new system to both applicants and educators. Given the favorable outcomes and broader uptake of the system among other specialties, the field of neurosurgery adopted the utilization of the ERAS-based program signaling and geographic preference for the first time for the 2022-2023 Residency Application Cycle and later opted to continue them for the 2023-2024 and 2024-2025 cycles. For the 2024-2025 Match Cycle, neurosurgery applicants have 25 signals, i.e., a "high-signal" approach, where non-signaled programs have a low interview conversion rate. This literature review discusses the rationale behind the change, the outcomes of other competitive specialties from prior cycles, the evolving nature of the change, and the potential impact on applicants and programs. As we describe in this review, signaling may potentially represent a surrogate form of an application cap. Other considerations relate to cost savings for both applicants and programs from a high-signal approach in neurosurgery. These modifications represent a foundational attempt to alleviate the application overburdening and non-holistic review in the residency application process, including for neurosurgery. While these changes have been a welcomed addition for all stakeholders in residency match cycles so far, further prospectively directed surveys along with qualitative research studies are warranted to better delineate the downstream impact of these changes and guide further optimization of the application system.

Indexed as

clinical trainingmedical educationneurosurgery residencynrmppostgraduate educationpostgraduate medical education (pgme)program signalresidencyresidency applicationresidency match

Identifiers

PMID39429415
PMCPMC11491130

What OpenQuestion holds

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Registered trials

None linked

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.