Evidence map›Paper›PMID 41552112›Full record

ArticleCureus2025

Integration of Large Language Models in Personal Statements for Neurosurgical Residency Applications: Insights from a Student Survey.

Cameron A Rivera, Vratko Himic, Nathan Zwagerman, Ashish H Shah, Michael Ivan, Ricardo J Komotar, Daniel M Aaronson

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Cameron A RiveraNeurological Surgery, University of Miami Miller School of Medicine, Miami, USA.
Vratko HimicNeurological Surgery, University of Miami Miller School of Medicine, Miami, USA.
Nathan ZwagermanNeurosurgery, Medical College of Wisconsin, Milwaukee, USA.
Ashish H ShahNeurological Surgery, University of Miami Miller School of Medicine, Miami, USA.
Michael IvanNeurological Surgery, University of Miami Miller School of Medicine, Miami, USA.
Ricardo J KomotarNeurological Surgery, University of Miami Miller School of Medicine, Miami, USA.
Daniel M AaronsonNeurological Surgery, University of Miami Miller School of Medicine, Miami, USA.

Funding

Multiparametric Volumetric MR Characterization of Intracranial LesionsR01CA172210 · NCI · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI MAUDSLEY, ANDREW A. · 2014 to 2018
$2.7M
Development and Maintenance of Software for MR Spectroscopic ImagingR01EB016064 · NIBIB · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI MAUDSLEY, ANDREW A. · 2013 to 2021
$2.0M
NCI NIH HHS R01 CA172210NIBIB NIH HHS R01 EB016064
6 · The paper itself

Abstract

Large language models (LLMs) are presumed to play an increasing role in residency applications. Given the role of personal statements in the holistic review of neurosurgical residency applicants, the extent of LLM influence remains unexplored. A multicenter survey was administered to residency interviewees during the 2024-2025 interview cycle. Respondents anonymously reported their use of LLM tools while writing personal statements. A total of 35 survey responses were recorded. All results were anonymized to protect applicant identity and any ensuing bias. Of the responses, 26% reported using LLM tools for minor edits in their personal statements, and no applicant reported the use of AI tools for major sections of their personal statements. It remains difficult to differentiate the use of AI tools for paragraph generation as opposed to minor editing tasks of the applicant's original work. As AI technologies continue to evolve, program directors should consider how LLM-assisted writing influences their ability to holistically assess an applicant and encourage more explicit guidelines for the use of these tools.

Indexed as

artificial intelligencelarge language modelneurosurgerypersonal statementresidency

Identifiers

PMID41552112
PMCPMC12807993

What OpenQuestion holds

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LicenceCC BY
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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.