Evidence map›Paper›PMID 42811190›Full record

ReviewNature medicine2026

Reproducibility in biomedical research.

Judy Zhong, Debra D'Angelo, Chen Lyu, Ian Xu, Peter K Enns, Fei Wang, Rainu Kaushal

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature medicine, 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

7 authors.

Judy ZhongDepartment of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA. juz4004@med.cornell.edu.ORCID http://orcid.org/0000-0002-2163-8447
Debra D'AngeloDepartment of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
Chen LyuDepartment of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.ORCID http://orcid.org/0000-0003-4406-5079
Ian XuMillburn High School, Millburn, NJ, USA.
Peter K EnnsDepartment of Government and Brooks School of Public Policy, Cornell University, Ithaca, NY, USA.
Fei WangDepartment of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.ORCID http://orcid.org/0000-0001-9459-9461
Rainu KaushalDepartment of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.ORCID http://orcid.org/0000-0002-4694-2517

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Reproducibility has long been considered a foundation of trustworthy science, yet practices that enable reproducible research remain inconsistently implemented, despite widespread awareness and establishment of policies and guidelines. Biomedical research that cannot be reproduced can lead to resource waste and adversely impact patient safety, public health outcomes, scientific progress and public trust. Rapid advances in technology, scientific dissemination and clinical translation have prompted renewed emphasis on validating research methods and findings to ensure transparency, rigor and credibility. Here we provide an overview of the current landscape of reproducible research in the biomedical sciences, summarize current efforts to harmonize terminology and implementation, and highlight the persistent barriers to adoption of best practices. We outline a practical framework to promote more uniform implementation and explore future directions for advancing reproducibility across stakeholders in the research process, including researchers, institutions, journals and funders.

Identifiers

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.