# A deep learning platform to evaluate the reliability of scientific claims by citation analysis.

> **NIH NIH R44** · SCITE, INC. · 2020 · $746,725

## Abstract

The opioid epidemic in the United States has been traced to a 1980 letter reporting in the
prestigious New England Journal of Medicine that synthetic opioids are not addictive. A belated
citation analysis led the journal to append this letter with a warning this letter has been “heavily
and uncritically cited” as evidence that addiction is rare with opioid therapy.” This epidemic is but
one example of how unreliable and uncritically cited scientific claims can affect public health, as
studies from industry report that a substantial part of biomedical reports cannot be
independently verified. Yet, there is no publicly available resource or indicator to determine how
reliable a scientific claim is without becoming an expert on the subject or retaining one. The total
citation count, the commonly used measure, is inherently a poor proxy for research quality
because confirming and refuting citations are counted as equal, while the prestige of the journal
is not a guarantee that a claim published there is true. The lack of indicators for the veracity of
reported claims costs the public, businesses, and governments, billions of dollars per year. We
have developed a prototype that automatically classifies statements citing a scientific claim into
three classes: those that provide supporting or contradicting evidence, or merely mention the
claim. This unique capability enables scite users to analyze the reliability of scientific claims at
an unprecedented scale and speed, helping them to make better-informed decisions. The
prototype has attracted potential customers among top biotechnology and pharmaceutical
companies, research institutions, academia, and academic publishers. We propose to conduct
research that will refine scite into an MVP by optimizing prototype efficiency and accuracy until
they reach feasible milestones, and will refine the product-market fit in our beachhead market,
academic publishing, whose influence on the integrity and reliability of research is difficult to
overestimate.

## Key facts

- **NIH application ID:** 10136941
- **Project number:** 4R44DA050155-02
- **Recipient organization:** SCITE, INC.
- **Principal Investigator:** Josh Nicholson
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $746,725
- **Award type:** 4N
- **Project period:** 2019-09-30 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10136941

## Citation

> US National Institutes of Health, RePORTER application 10136941, A deep learning platform to evaluate the reliability of scientific claims by citation analysis. (4R44DA050155-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10136941. Licensed CC0.

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