Evidence map›Paper›PMID 42130706›Full record

ArticleAnaesthesia reports

Evaluating the quality of expert-trained generative artificial intelligence when answering tracheostomy-related questions.

B Gorst, E Johnstone, J Abbas, M Moran, A Payton, B A McGrath

Abstract read
In one paragraph

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

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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

6 authors.

B GorstSchool of Biological Sciences, Faculty of Biology, Medicine and Health University of Manchester Manchester UK.
E JohnstoneDivision of Informatics, Imaging and Data Sciences University of Manchester Manchester UK.ORCID https://orcid.org/0009-0009-3321-4615
J AbbasNorth West School of Anaesthetics Liverpool UK.ORCID https://orcid.org/0000-0002-7870-2980
M MoranManchester Academic Critical Care, Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health University of Manchester, Manchester Academic Health Science Centre Manchester UK.
A PaytonDivision of Informatics, Imaging and Data Sciences University of Manchester Manchester UK.ORCID https://orcid.org/0000-0003-0335-152X
B A McGrathManchester Academic Critical Care, Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health University of Manchester, Manchester Academic Health Science Centre Manchester UK.ORCID https://orcid.org/0000-0003-3048-3480

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tracheostomy training is effective, yet significant face-to-face training barriers remain. We aimed to develop, train and test a generative artificial intelligence instructor (AI-Brendan). AI-Brendan utilises the base model of ChatGPT-4.0, augmented by a curated dataset of expert-answered tracheostomy care questions, delivered with a contextually responsive conversational interface (Inworld). AI-Brendan's responses to five randomly selected questions were compared with ChatGPT-4.0 and five human expert responses using a modified Ensuring Quality Information for Patients tool, rated by six blinded independent adjudicators. A total of 450 Ensuring Quality Information for Patients scores were generated from responses (150 each from AI-Brendan, ChatGPT-4.0 and Experts), with no alarming responses. AI-Brendan (mean score 12.6 ± 3.0) performed significantly better than ChatGPT-4.0 (11.4 ± 2.4) and Experts (8.9 ± 3.6, ANOVA p < 0.01). Friedman testing demonstrated no significant differences across repeated responses, indicating consistency. Within-question variability indicated highly consistent responses. Inter-rater reliability was good (Cronbach's α = 0.863). Adjudicator's ability to correctly determine answer source was 50% for Experts, 39% for AI-Brendan and 33% for ChatGPT-4.0. Our expert-trained generative artificial intelligence produced "better" answers than open-source ChatGPT-4.0 and experts when rated by the modified Ensuring Quality Information for Patients tool. A validated artificial intelligence instructor has significant implications for addressing training barriers, increasing efficacy, overcoming scheduling, financial or geographical limitations, offering consistent, accessible and flexible training.

Indexed as

artificial intelligencereproducibility of resultstracheostomy

Identifiers

PMID42130706
PMCPMC13163142

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.