Evidence map›Paper›PMID 42620388›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Moving from Bench to Bedside: A Social History of Actionability in Biomedicine.

Kellie Owens, Ari Z Klein, Graciela Gonzalez-Hernandez

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

3 authors.

Kellie OwensDepartment of Population Health, New York University, New York, NY, USA.
Ari Z KleinDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
Graciela Gonzalez-HernandezDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

Funding

Conceptualizing Actionability in Clinical Genomic ScreeningR00HG010905 · NHGRI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI OWENS, KELLIE NICOLE · 2022 to 2024
$727k
NHGRI NIH HHS R00 HG010905
6 · The paper itself

Abstract

Background: As medicine grows increasingly technological and scientific, biomedical researchers working to bring new knowledge to bear on clinical practice face a key question: when is a new intervention or treatment ready for clinical use? Because this is both a technical and ethical dilemma, it is crucial to examine the social history of how different approaches to this question emerge and the values or assumptions they embed. Methods: We examine the rise and proliferation of an increasingly common framework for assessing the value of new biomedical data or technology, "actionability," through a computational analysis of published scientific literature referencing this and related terms, including topic modeling and Medical Subject Headings (MeSH) term analysis of over 7000 scientific abstracts indexed in PubMed. Results: We find that actionability, as a term, began appearing more commonly in published literature in the mid-2000s, and proliferated throughout the 2010s and into the 20s. While originally used primarily in research on healthcare quality and implementation, the concept's rise in popularity is ultimately driven by uptake in the fields of clinical genetics and oncology. Conclusions: The adoption of actionability in these fields suggests that actionability as a conceptual framework may be most valuable to areas of translational medicine seeking to make sense of increasing amounts of data and technological innovation with differing levels of scientific validity and clinical utility. Recognizing this value, we also caution that actionability drives our attention primarily towards whether a test or piece of information can lead to action, not whether that action has proven benefits. As clinicians and researchers face difficult questions about how to sort through growing amounts of data to generate knowledge that can have a real impact on patient health, empirical bioethics should play a key role in analyzing the trade-offs and impacts of different approaches.

Identifiers

PMID42620388
PMCPMC13484758

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.