Preservation of deep epigastric perforators during anterior component separation technique (ACST) results in equivalent wound complications compared to transversus abdominis release (TAR)

Sacco, Jana; Ayuso, Sullivan A.; Salvino, M; Scarola, G; Ku, D; Tawkaliyar, R; Brown, Kevin R.; Colavita, Paul D.; Kercher, Kent W.; Augenstein, Vedra A.; Heniford, B. Todd

doi:10.1007/s10029-023-02811-1

Cited by 6 publications

(2 citation statements)

References 38 publications

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“…OpenAI ChatGPT (versions 3.5 and 4.0; hereafter referred to as chatbot 1 and chatbot 2) was trained to generate medical abstracts based on provided abstracts as examples. The research residents and senior attending physician identified 10 abstracts 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 by our group from 2012 to 2022 that were presented at national meetings and published in surgical journals to serve as the training models. There was variation in the first author of each abstract, a junior trainee, but all studies had the same senior author (B.T.H.).…”

Section: Methodsmentioning

confidence: 99%

Comparison of Medical Research Abstracts Written by Surgical Trainees and Senior Surgeons or Generated by Large Language Models

Holland,

Lorenz,

Cavanagh

et al. 2024

JAMA Netw Open

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ImportanceArtificial intelligence (AI) has permeated academia, especially OpenAI Chat Generative Pretrained Transformer (ChatGPT), a large language model. However, little has been reported on its use in medical research.ObjectiveTo assess a chatbot’s capability to generate and grade medical research abstracts.Design, Setting, and ParticipantsIn this cross-sectional study, ChatGPT versions 3.5 and 4.0 (referred to as chatbot 1 and chatbot 2) were coached to generate 10 abstracts by providing background literature, prompts, analyzed data for each topic, and 10 previously presented, unassociated abstracts to serve as models. The study was conducted between August 2023 and February 2024 (including data analysis).ExposureAbstract versions utilizing the same topic and data were written by a surgical trainee or a senior physician or generated by chatbot 1 and chatbot 2 for comparison. The 10 training abstracts were written by 8 surgical residents or fellows, edited by the same senior surgeon, at a high-volume hospital in the Southeastern US with an emphasis on outcomes-based research. Abstract comparison was then based on 10 abstracts written by 5 surgical trainees within the first 6 months of their research year, edited by the same senior author.Main Outcomes and MeasuresThe primary outcome measurements were the abstract grades using 10- and 20-point scales and ranks (first to fourth). Abstract versions by chatbot 1, chatbot 2, junior residents, and the senior author were compared and judged by blinded surgeon-reviewers as well as both chatbot models. Five academic attending surgeons from Denmark, the UK, and the US, with extensive experience in surgical organizations, research, and abstract evaluation served as reviewers.ResultsSurgeon-reviewers were unable to differentiate between abstract versions. Each reviewer ranked an AI-generated version first at least once. Abstracts demonstrated no difference in their median (IQR) 10-point scores (resident, 7.0 [6.0-8.0]; senior author, 7.0 [6.0-8.0]; chatbot 1, 7.0 [6.0-8.0]; chatbot 2, 7.0 [6.0-8.0]; P = .61), 20-point scores (resident, 14.0 [12.0-7.0]; senior author, 15.0 [13.0-17.0]; chatbot 1, 14.0 [12.0-16.0]; chatbot 2, 14.0 [13.0-16.0]; P = .50), or rank (resident, 3.0 [1.0-4.0]; senior author, 2.0 [1.0-4.0]; chatbot 1, 3.0 [2.0-4.0]; chatbot 2, 2.0 [1.0-3.0]; P = .14). The abstract grades given by chatbot 1 were comparable to the surgeon-reviewers’ grades. However, chatbot 2 graded more favorably than the surgeon-reviewers and chatbot 1. Median (IQR) chatbot 2-reviewer grades were higher than surgeon-reviewer grades of all 4 abstract versions (resident, 14.0 [12.0-17.0] vs 16.9 [16.0-17.5]; P = .02; senior author, 15.0 [13.0-17.0] vs 17.0 [16.5-18.0]; P = .03; chatbot 1, 14.0 [12.0-16.0] vs 17.8 [17.5-18.5]; P = .002; chatbot 2, 14.0 [13.0-16.0] vs 16.8 [14.5-18.0]; P = .04). When comparing the grades of the 2 chatbots, chatbot 2 gave higher median (IQR) grades for abstracts than chatbot 1 (resident, 14.0 [13.0-15.0] vs 16.9 [16.0-17.5]; P = .003; senior author, 13.5 [13.0-15.5] vs 17.0 [16.5-18.0]; P = .004; chatbot 1, 14.5 [13.0-15.0] vs 17.8 [17.5-18.5]; P = .003; chatbot 2, 14.0 [13.0-15.0] vs 16.8 [14.5-18.0]; P = .01).Conclusions and RelevanceIn this cross-sectional study, trained chatbots generated convincing medical abstracts, undifferentiable from resident or senior author drafts. Chatbot 1 graded abstracts similarly to surgeon-reviewers, while chatbot 2 was less stringent. These findings may assist surgeon-scientists in successfully implementing AI in medical research.

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Section: Methodsmentioning

confidence: 99%

Comparison of Medical Research Abstracts Written by Surgical Trainees and Senior Surgeons or Generated by Large Language Models

Holland,

Lorenz,

Cavanagh

et al. 2024

JAMA Netw Open

View full text Add to dashboard Cite

show abstract

“…If the fascia does not come together easily, we start with a unilateral or bilateral posterior rectus sheath release (PRSR) and retrorectus dissection. At this point, we reassess fascial tension and defect width in order to decide whether to perform a TAR or a perforator-sparing anterior component separation (PS-ACST) of the external oblique . As the authors recognize, the advancement of the TAR beyond the PRSR and retrorectus dissection contributes little to the closure of the anterior fascia.…”

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confidence: 99%