Evidence map›Paper›PMID 40574724›Full record

ArticleThe Laryngoscope2025

Evaluating Resident Feedback Using a Large Language Model: Are We Missing Core Competencies?

Syed Ameen Ahmad, Maria Armache, Danielle R Trakimas, Jenny X Chen, Deepa Galaiya

Abstract read
In one paragraph

Article in The Laryngoscope, 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

5 authors.

Syed Ameen AhmadJohns Hopkins University School of Medicine, Baltimore, Maryland, USA.ORCID 0000-0002-6179-5252
Maria ArmacheDepartment of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Danielle R TrakimasDepartment of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Jenny X ChenDepartment of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.ORCID 0000-0002-2992-4771
Deepa GalaiyaDepartment of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.ORCID 0000-0002-7221-9883

Funding

Research Training in Otolaryngology ProgramR25DC021243 · NIDCD · JOHNS HOPKINS UNIVERSITY · PI John Patrick Carey, Amanda M. Lauer · 2024 to 2026
$1.2M
NIDCD NIH HHS R25 DC021243
6 · The paper itself

Abstract

objectivesUse a large language model (LLM) to examine the content and quality of narrative feedback provided to residents through: (1) an app collecting workplace-based assessments of surgical performance (SIMPL-OR), (2) Objective Structured Assessment of Technical Skills (OSATS), and (3) end-of-rotation (EOR) evaluations.

methodsNarrative feedback provided to residents at a single institution from 2017 to 2021 was examined. Sixty entries (20 of each format) were evaluated by two faculty members on whether they were encouraging, corrective, or specific, and whether they addressed the Core Competencies outlined by the Accreditation Council for Graduate Medical Education. ChatGPT4o was tested on these 60 entries before evaluating the remaining 776 entries.

resultsChatGPT evaluated entries with 90% concordance with faculty (κ = 0.94). Within the 776 feedback entries evaluated by ChatGPT, competencies addressed included: patient care (n = 491, 97% vs. 77% vs. 36% for SIMPL-OR, OSATS, EOR respectively, p < 0.001), practice-based learning (n = 175, 32% vs. 23% vs. 16%, p < 0.001), professionalism (n = 168, 1% vs. 6% vs. 40%, p < 0.001), medical knowledge (n = 95, 7% vs. 8% vs. 17%, p < 0.001), interpersonal and communication skills (n = 59, 3% vs. 3% vs. 12%, p < 0.001), and systems-based practice (n = 31, 4% vs. 2% vs. 5%, p = 0.387). Feedback was "encouraging" in 93% of both SIMPL-OR and OSATS, as compared to 84% of EOR (p < 0.001). Feedback was "corrective" in 71% of SIMPL-OR versus 44% of OSATS versus 24% of EOR (p < 0.001), and "specific" in 97% versus 53% versus 15%, respectively (p < 0.001).

conclusionDifferent instruments provided feedback of differing content and quality and a multimodal feedback approach is important. LEVEL OF EVIDENCE: N/A.

Indexed as

Clinical CompetenceEducational MeasurementEducation, Medical, GraduateFormative FeedbackInternship and ResidencyFeedbackHumansLanguageLarge Language Modelsmedical educationnatural language processingresident education

Identifiers

PMID40574724
PMCPMC12221221

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.