Evidence map›Paper›PMID 39301304›Full record

ArticlePlastic and reconstructive surgery. Global open2024

Class in Session: Analysis of GPT-4-created Plastic Surgery In-service Examination Questions.

Daniel Najafali, Logan G Galbraith, Justin M Camacho, Victoria Stoffel, Isabel Herzog, Civanni Moss, Stephanie L Taiberg, Leonard Knoedler

Abstract read
In one paragraph

Article in Plastic and reconstructive surgery. Global open, 2024. 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. Artificial Intelligence and Plastic Surgery Resident Education.Plastic and reconstructive surgery. Global open · 2025
    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

8 authors.

Daniel NajafaliFrom the Carle Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, Ill.
Logan G GalbraithNortheast Ohio Medical University College of Medicine, Rootstown, Ohio.
Justin M CamachoDrexel University College of Medicine, Philadelphia, Pa.
Victoria StoffelDrexel University College of Medicine, Philadelphia, Pa.
Isabel HerzogRutgers New Jersey School of Medicine, Newark, N.J.
Civanni MossTemple University Lewis Katz School of Medicine, Philadelphia, Pa.
Stephanie L TaibergChicago Medical School at Rosalind Franklin University of Medicine and Science, Chicago, Ill.
Leonard KnoedlerDepartment of Plastic, Hand and Reconstructive Surgery, University Hospital Regensburg, Regensburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The Plastic Surgery In-Service Training Examination (PSITE) remains a critical milestone in residency training. Successful preparation requires extensive studying during an individual's residency. This study focuses on the capacity of Generative Pre-trained Transformer 4 (GPT-4) to generate PSITE practice questions. Methods: GPT-4 was prompted to generate multiple choice questions for each PSITE section and provide answer choices with detailed rationale. Question composition via readability metrics were analyzed, along with quality. Descriptive statistics compared GPT-4 and the 2022 PSITE. Results: The overall median Flesch-Kincaid reading ease for GPT-4-generated questions was 43.90 (versus 50.35 PSITE, Conclusions: Our study found that GPT-4 can be adapted to generate practice questions for the 2022 PSITE, but its questions are of poor quality. The program can offer general explanations for both the correct and incorrect answer options but was observed to generate false information and poor-quality explanations. Although trainees should navigate with caution as the technology develops, GPT-4 has the potential to serve as an effective educational adjunct under the supervision of trained plastic surgeons.

Identifiers

PMID39301304
PMCPMC11412705

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.