ArticleCritical care explorations2026
Hallucination Rate of Peer-Reviewed Citations Generated by Large Language Models in Neurocritical Care.
Article in Critical care explorations, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
importanceLarge language models (LLMs) are increasingly used for scientific literature retrieval, yet their citation accuracy in specialized clinical domains remains poorly characterized. In neurocritical care (NCC), fabricated or inaccurate citations may be difficult to detect without deliberate verification.
objectivesTo evaluate hallucination and fabrication rates of peer-reviewed citations generated by three LLMs across core NCC topics, under constrained zero-shot, memory-only conditions. DESIGN, SETTING, AND
participantsIn this cross-sectional, blinded technology performance evaluation, Generative Pretrained Transformer (GPT)-5.3, DeepSeek-V3, and Grok-4 were queried on March 10, 2026, under identical zero-shot, retrieval-disabled web-interface conditions. Ten NCC topics were submitted to each model, and each model generated 10 references per topic, yielding 300 references. MAIN OUTCOMES AND MEASURES: Two NCC experts, blinded to model identity, independently verified each reference against PubMed, DOI, Google Scholar, and CrossRef and scored accuracy using a Hallucination Scale (0-3). The primary outcome was any hallucination, defined as any citation inaccuracy. The secondary outcome was fabrication, defined as a nonexisting complete bibliographic entity.
resultsInter-rater agreement was excellent (κ = 0.91; 95% CI, 0.86-0.96). Overall, 165 of 300 references (55.0%) contained a citation inaccuracy, and 85 of 300 (28.3%) were completely fabricated. DeepSeek-V3 had the lowest hallucination rate (23%; fabrication 8%), followed by GPT-5.3 (69%; fabrication 27%) and Grok-4 (73%; fabrication 50%). Compared with DeepSeek-V3, Grok-4 was 3.17 times more likely to hallucinate (95% CI, 2.03-4.96; p < 0.001), and GPT-5.3 was 3.00 times more likely to hallucinate (95% CI, 1.94-4.63; p < 0.001). Topic-level findings were exploratory and should be interpreted cautiously. CONCLUSIONS AND RELEVANCE: Under standardized zero-shot, retrieval-disabled web-interface conditions, LLMs generated substantial numbers of inaccurate and fabricated NCC citations. Because fabricated references can appear complete and credible, artificial intelligence-generated citations should be verified across reliable databases before use in clinical, educational, or scholarly work.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.