ArticlePNAS nexus2024
Hidden citations obscure true impact in science.
Article in PNAS nexus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed.
- Mapping the global research landscape and knowledge structure of robotic-assisted dental, oral and maxillofacial surgery: a bibliometric analysis.Journal of robotic surgery · 2026Review
- Microenvironment-Activated Fe-MOF Nanoplatform Enables Controlled Doxorubicin Release and Ferroptosis-Associated Oxidative Damage in MCF-7 Breast Cancer Cells.International journal of nanomedicine · 2026Article
- Is Addiction Research Addicted to Artificial Intelligence? Mapping the Intersection of Artificial Intelligence, Substance Use and Mental Health Through a Bibliometric Analysis.Drug and alcohol review · 2026Review
- Modeling the Evolution of Dynamic Triadic Closure Under Superlinear Growth and Node Aging in Citation Networks.Entropy (Basel, Switzerland) · 2025Article
- The rise of artificial intelligence for cognitive behavioral therapy: A bibliometric overview.Applied psychology. Health and well-being · 2025Review
- Quantifying the impact of biobanks and cohort studies.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A bibliometric analysis of the top 100 most cited articles on pineal parenchymal tumors worldwide.Brain & spine · 2025Article
- A network-based normalized impact measure reveals successful periods of scientific discovery across discipline.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
References, the mechanism scientists rely on to signal previous knowledge, lately have turned into widely used and misused measures of scientific impact. Yet, when a discovery becomes common knowledge, citations suffer from obliteration by incorporation. This leads to the concept of hidden citation, representing a clear textual credit to a discovery without a reference to the publication embodying it. Here, we rely on unsupervised interpretable machine learning applied to the full text of each paper to systematically identify hidden citations. We find that for influential discoveries hidden citations outnumber citation counts, emerging regardless of publishing venue and discipline. We show that the prevalence of hidden citations is not driven by citation counts, but rather by the degree of the discourse on the topic within the text of the manuscripts, indicating that the more discussed is a discovery, the less visible it is to standard bibliometric analysis. Hidden citations indicate that bibliometric measures offer a limited perspective on quantifying the true impact of a discovery, raising the need to extract knowledge from the full text of the scientific corpus.
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.