Evidence map›Paper›PMID 38762669›Full record

ArticleNPJ digital medicine2024

A validated web-application (GFDC) for automatic classification of glaucomatous visual field defects using Hodapp-Parrish-Anderson criteria.

Arun James Thirunavukarasu, Nikhil Jain, Rohan Sanghera, Federico Lattuada, Shathar Mahmood, Anna Economou, Helmut C Y Yu, Rupert Bourne

Abstract read
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 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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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

6 citing papers in PubMed.

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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.

Arun James ThirunavukarasuUniversity of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge, UK. ajt205@cantab.ac.uk.ORCID http://orcid.org/0000-0001-8968-4768
Nikhil JainCambridge Eye Research Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Rohan SangheraUniversity of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0001-6370-8426
Federico LattuadaDepartment of Engineering, University of Cambridge, Cambridge, UK.
Shathar MahmoodUniversity of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge, UK.
Anna EconomouUniversity of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0001-6836-4159
Helmut C Y YuCambridge Eye Research Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Rupert BourneCambridge Eye Research Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK. rupert.bourne1@nhs.net.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Subjectivity and ambiguity of visual field classification limits the accuracy and reliability of glaucoma diagnosis, prognostication, and management decisions. Standardised rules for classifying glaucomatous visual field defects exist, but these are labour-intensive and therefore impractical for day-to-day clinical work. Here a web-application, Glaucoma Field Defect Classifier (GFDC), for automatic application of Hodapp-Parrish-Anderson, is presented and validated in a cross-sectional study. GFDC exhibits perfect accuracy in classifying mild, moderate, and severe glaucomatous field defects. GFDC may thereby improve the accuracy and fairness of clinical decision-making in glaucoma. The application and its source code are freely hosted online for clinicians and researchers to use with glaucoma patients.

Identifiers

PMID38762669
PMCPMC11102533

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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.