ArticlePatterns (New York, N.Y.)2022
Deep forecasting of translational impact in medical research.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Could agentic AI topple grant-funding systems?Nature · 2026Article
- Uncovering drivers of climate research in policy with pretrained language models.Patterns (New York, N.Y.) · 2025Article
- PTNS: patent citation trajectory prediction based on temporal network snapshots.Scientific reports · 2024Article
- What predicts citation counts and translational impact in headache research? A machine learning analysis.Cephalalgia : an international journal of headache · 2024Article
- Machine learning to promote translational research: predicting patent and clinical trial inclusion in dementia research.Brain communications · 2024Article
- Prioritising and incentivising productivity within indicator-based approaches to Research Impact Assessment: a commentary.Health research policy and systems · 2023Article
- Exploring the application of machine learning to expert evaluation of research impact.PloS one · 2023Article
- Reframing research impact.Patterns (New York, N.Y.) · 2022Article
- The State of Artificial Intelligence in Skin Cancer Publications.Journal of cutaneous medicine and surgeryArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The value of biomedical research-a $1.7 trillion annual investment-is ultimately determined by its downstream, real-world impact, whose predictability from simple citation metrics remains unquantified. Here we sought to determine the comparative predictability of future real-world translation-as indexed by inclusion in patents, guidelines, or policy documents-from complex models of title/abstract-level content versus citations and metadata alone. We quantify predictive performance out of sample, ahead of time, across major domains, using the entire corpus of biomedical research captured by Microsoft Academic Graph from 1990-2019, encompassing 43.3 million papers. We show that citations are only moderately predictive of translational impact. In contrast, high-dimensional models of titles, abstracts, and metadata exhibit high fidelity (area under the receiver operating curve [AUROC] > 0.9), generalize across time and domain, and transfer to recognizing papers of Nobel laureates. We argue that content-based impact models are superior to conventional, citation-based measures and sustain a stronger evidence-based claim to the objective measurement of translational potential.
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.