Evidence map›Paper›PMID 38110504›Full record

ArticleScientific reports2023

Publisher Correction: Quantifying and correcting bias due to outcome dependent self-reported weights in longitudinal study of weight loss interventions.

Jiayi Tong, Rui Duan, Ruowang Li, Chongliang Luo, Jason H Moore, Jingsan Zhu, Gary D Foster, Kevin G Volpp, William S Yancy, Pamela A Shaw and 1 more

Abstract readPublished Erratum
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2023. 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

5 · Who and what money

Authors and funding

11 authors.

Jiayi TongDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Rui DuanDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, 02115, USA.
Ruowang LiDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Chongliang LuoDivision of Public Health Sciences, Department of Surgery, Washington University in St. Louis, St. Louis, MO, 63110, USA.
Jason H MooreDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Jingsan ZhuCenter for Health Incentives and Behavioral Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Gary D FosterWW International, New York, NY, 10010, USA.
Kevin G VolppDepartment of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
William S YancyDepartment of Medicine, Duke University, Durham, NC, 27705, USA.
Pamela A ShawDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Yong ChenDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. ychen123@upenn.edu.

Funding

Federated and transfer learning methods for cross-ancestry and cross-phenotype integration of genomic datasetsR01GM148494 · NIGMS · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Rui Duan · 2023 to 2026
$1.7M
NIGMS NIH HHS R01 GM148494
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Identifiers

PMID38110504
PMCPMC10728146

What OpenQuestion holds

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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.