Evidence map›Paper›PMID 35325131›Full record

ArticleILAR journal2021

ICLAS LAQ Network for the Promotion of Animal Quality in Research.

Patri Vergara, Gregory Ballard, Cynthia Besch-Williford, Nobuhito Hayashimoto, Cynthia Pekow, Ana Perez, Katja Schmidt, William Shek, Martin Toft, Atsushi Yoshiki

Abstract read
In one paragraph

Article in ILAR journal, 2021. 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

10 authors.

Patri VergaraVeterinary School, Universitat Autonoma de Barcelona, Barcelona, Spain.
Gregory BallardThe Jackson Laboratory, Bar Harbor, Maine, USA.
Cynthia Besch-WillifordIDEXX BioAnalytics, Columbia, Missouri, USA.
Nobuhito HayashimotoCentral Institute for Experimental Animals, Kawasaki, Japan.
Cynthia PekowUniversity of Washington, Seatle, Washington, USA.
Ana PerezHumodigen, Burlingame, California, USA.
Katja SchmidtGerman Cancer Research Center (DKFZ), Heidelberg, Germany.
William ShekCharles River Laboratories (RADS), Wilmington, USA.
Martin ToftQM Diagnostics, Radboud University, Radboud, Netherlands.
Atsushi YoshikiRIKEN BioResource Research Center, Ibaraki Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

ICLAS Laboratory Animal Quality Network (LAQN) programs currently consist of the Performance Evaluation Program (PEP), which focuses on microbial monitoring by and for laboratory animal diagnostic laboratories, and the Genetic Reference Monitoring Program (GENRef), which provides assay-ready reference DNA for genetic testing of mouse strains. Since 2008, PEP has grown to become a truly international program with participating laboratories in 5 continents. Launched in 2016, GENRef currently distributes DNA from 12 common inbred mouse strains for use in genetic monitoring of locally inbred colonies as well as for genetic testing of stocks, particularly genetically engineered stocks, of uncertain origins. GENRef has the capacity to include additional strains as well as additional species. PEP and GENRef provide the reagents at cost, as a resource to the international scientific community, in the interest of improving research quality in an environment of growing concern for research quality, rigor, and reproducibility.

Indexed as

Animals, LaboratoryGenetic EngineeringAnimalsLaboratoriesMiceReproducibility of Resultsanimal modelsdiagnostic testinggenetic testingmicequality controlratsreproducibility of resultsvirus

Identifiers

PMID35325131
PMCPMC9803977

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.