Evidence map›Paper›PMID 37514101›Full record

ArticlePharmaceutics2023

Evaluation of [

Jin Hui Teh, Ala Amgheib, Ruisi Fu, Chris Barnes, Joel Abrahams, Ali Ashek, Ning Wang, Zixuan Yang, Muneera Mansoorudeen, Nicholas J Long and 1 more

Open access · goldAbstract read
In one paragraph

Article in Pharmaceutics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.7field-weighted citation impact, top 29% of its field
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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Review
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

11 authors at 1 institution in 1 country.

Jin Hui TehDepartment of Chemistry, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, UK.ORCID 0000-0002-0014-523X
Ala AmgheibDepartment of Surgery and Cancer, Imperial Centre for Translational and Experimental Medicine, Imperial College London, London W12 0NN, UK.
Ruisi FuDepartment of Surgery and Cancer, Imperial Centre for Translational and Experimental Medicine, Imperial College London, London W12 0NN, UK.
Chris BarnesDepartment of Surgery and Cancer, Imperial Centre for Translational and Experimental Medicine, Imperial College London, London W12 0NN, UK.
Joel AbrahamsDepartment of Surgery and Cancer, Imperial Centre for Translational and Experimental Medicine, Imperial College London, London W12 0NN, UK.
Ali AshekDepartment of Surgery and Cancer, Imperial Centre for Translational and Experimental Medicine, Imperial College London, London W12 0NN, UK.
Ning WangDepartment of Surgery and Cancer, Imperial Centre for Translational and Experimental Medicine, Imperial College London, London W12 0NN, UK.
Zixuan YangDepartment of Surgery and Cancer, Imperial Centre for Translational and Experimental Medicine, Imperial College London, London W12 0NN, UK.
Muneera MansoorudeenDepartment of Surgery and Cancer, Imperial Centre for Translational and Experimental Medicine, Imperial College London, London W12 0NN, UK.
Nicholas J LongDepartment of Chemistry, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, UK.
Eric O AboagyeDepartment of Surgery and Cancer, Imperial Centre for Translational and Experimental Medicine, Imperial College London, London W12 0NN, UK.ORCID 0000-0003-2276-6771
Imperial College London · GB

Funding

Medical Research Council MR/N020782/1
6 · The paper itself

Abstract

C-Met is a receptor tyrosine kinase that is overexpressed in a range of different cancer types, and has been identified as a potential biomarker for cancer imaging and therapy. Previously, a

Indexed as

[18F]AlFc-MetPET/CTtyrosine kinase receptors

Identifiers

PMID37514101
PMCPMC10383791
OpenAlexW4383877644

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.