Article in JMIR infodemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.
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
13 authors.
Natansh D ModiClinical and Health Sciences, University of South Australia, GPO Box 2471, Adelaide, South Australia, 5001, Australia, 61 08 830 24926.ORCID 0000-0003-3262-5374
Cyril A AlexCollege of Medicine and Public Health, Flinders Health and Medical Research Institute, Flinders University, Adelaide, South Australia, Australia.ORCID 0009-0001-5638-4328
Abdulhalim A AwatyCollege of Medicine and Public Health, Flinders Health and Medical Research Institute, Flinders University, Adelaide, South Australia, Australia.ORCID 0009-0001-8863-5160
Bradley D MenzCollege of Medicine and Public Health, Flinders Health and Medical Research Institute, Flinders University, Adelaide, South Australia, Australia.ORCID 0000-0002-0855-5081
Stephen BacchiAdelaide Medical School, Faculty of Health and Medical Science, The University of Adelaide, Adelaide, South Australia, Australia.ORCID 0000-0001-5130-8628
Kacper T GradonDepartment of Security and Crime Science, University College London, London, England, United Kingdom.ORCID 0000-0003-0750-8678
Jessica M LoganClinical and Health Sciences, University of South Australia, GPO Box 2471, Adelaide, South Australia, 5001, Australia, 61 08 830 24926.ORCID 0000-0003-4046-6908
Andrew RowlandCollege of Medicine and Public Health, Flinders Health and Medical Research Institute, Flinders University, Adelaide, South Australia, Australia.ORCID 0000-0002-8946-3954
Lisa Kalisch EllettClinical and Health Sciences, University of South Australia, GPO Box 2471, Adelaide, South Australia, 5001, Australia, 61 08 830 24926.ORCID 0000-0001-5063-6128
Ross A McKinnonCollege of Medicine and Public Health, Flinders Health and Medical Research Institute, Flinders University, Adelaide, South Australia, Australia.ORCID 0000-0002-3725-793X
Michael D WieseClinical and Health Sciences, University of South Australia, GPO Box 2471, Adelaide, South Australia, 5001, Australia, 61 08 830 24926.ORCID 0000-0002-3255-9242
Michael J SorichCollege of Medicine and Public Health, Flinders Health and Medical Research Institute, Flinders University, Adelaide, South Australia, Australia.ORCID 0000-0003-1999-866X
Ashley M HopkinsCollege of Medicine and Public Health, Flinders Health and Medical Research Institute, Flinders University, Adelaide, South Australia, Australia.ORCID 0000-0001-7652-4378
Funding
No grant is acknowledged in the PubMed record.
6 · The paper itself
Abstract
Unlabelled: This cross-sectional evaluation of six consumer-facing large language model platforms found significant heterogeneity in safeguard performance against the generation of health disinformation, with Claude and ChatGPT demonstrating complete resistance across all prompt types, while Copilot, Meta AI, Grok, and Gemini exhibited substantial vulnerabilities.
Indexed as
DisinformationLarge Language ModelsCross-Sectional StudiesHumansdisinformationepidemiologylarge language modelsmisinformationpublic health
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.
Cross-Sectional Evaluation of Medical Disinformation Safeguards in Consumer-Facing Large Language Model Platforms. · full record | OpenQuestion