Evidence map›Paper›PMID 39511374›Full record

ArticleNPJ digital medicine2024

Automated decision making in Barrett's oesophagus: development and deployment of a natural language processing tool.

Agathe Zecevic, Laurence Jackson, Xinyue Zhang, Polychronis Pavlidis, Jason Dunn, Nigel Trudgill, Shahd Ahmed, Pierfrancesco Visaggi, Zanil YoonusNizar, Angus Roberts and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 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. 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

11 authors.

Agathe ZecevicGastroenterology Department, Guy's and St. Thomas' NHS Foundation Trust, London, SE1 7EH, UK.
Laurence JacksonClinical Scientific Computing, Guy's and St. Thomas' NHS Foundation Trust, London, SE1 7EH, UK.
Xinyue ZhangBiostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.
Polychronis PavlidisDepartment of Gastroenterology, King's College Hospital, London, Denmark Hill, SE5 9RS, London, WC2B 4BG, UK.
Jason DunnGastroenterology Department, Guy's and St. Thomas' NHS Foundation Trust, London, SE1 7EH, UK.
Nigel TrudgillGastroenterology Department, Sandwell and West Birmingham NHS Trust, West Bromwich, B71 4HJ, UK.
Shahd AhmedGastroenterology Department, Sandwell and West Birmingham NHS Trust, West Bromwich, B71 4HJ, UK.
Pierfrancesco VisaggiGastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy.
Zanil YoonusNizarGastroenterology Department, Guy's and St. Thomas' NHS Foundation Trust, London, SE1 7EH, UK.
Angus RobertsBiostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.ORCID http://orcid.org/0000-0002-4570-9801
Sebastian S ZekiGastroenterology Department, Guy's and St. Thomas' NHS Foundation Trust, London, SE1 7EH, UK. Sebastian.zeki@gstt.nhs.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Manual decisions regarding the timing of surveillance endoscopy for premalignant Barrett's oesophagus (BO) is error-prone. This leads to inefficient resource usage and safety risks. To automate decision-making, we fine-tuned Bidirectional Encoder Representations from Transformers (BERT) models to categorize BO length (EndoBERT) and worst histopathological grade (PathBERT) on 4,831 endoscopy and 4,581 pathology reports from Guy's and St Thomas' Hospital (GSTT). The accuracies for EndoBERT test sets from GSTT, King's College Hospital (KCH), and Sandwell and West Birmingham Hospitals (SWB) were 0.95, 0.86, and 0.99, respectively. Average accuracies for PathBERT were 0.93, 0.91, and 0.92, respectively. A retrospective analysis of 1640 GSTT reports revealed a 27% discrepancy between endoscopists' decisions and model recommendations. This study underscores the development and deployment of NLP-based software in BO surveillance, demonstrating high performance at multiple sites. The analysis emphasizes the potential efficiency of automation in enhancing precision and guideline adherence in clinical decision-making.

Identifiers

PMID39511374
PMCPMC11544096

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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.