Evidence map›Paper›PMID 38980826›Full record

ReviewGM crops & food2024

Best practices for acceptability of GM crops field trials conclusions: lessons for Africa.

Paul Chege, Julia Njagi, John Komen, Godfrey Ngure, John Muriuki, Margaret Karembu

Abstract readReview
In one paragraph

Review in GM crops & food, 2024. 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
–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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Paul ChegeProgram for Biosafety Systems (PBS), International Service for the Acquisition of Agri-biotech Applications (ISAAA AfriCenter), Nairobi, Kenya.
Julia NjagiInspection, National Biosafety Authority (NBA), Nairobi, Kenya.
John KomenProgram for Biosafety Systems (PBS), International Food Policy Institute (IFPRI), Washington, WA, USA.
Godfrey NgureProgram for Biosafety Systems (PBS), International Service for the Acquisition of Agri-biotech Applications (ISAAA AfriCenter), Nairobi, Kenya.
John MuriukiEnvironmental Science, Kenyatta University, Nairobi, Kenya.
Margaret KarembuProgram for Biosafety Systems (PBS), International Service for the Acquisition of Agri-biotech Applications (ISAAA AfriCenter), Nairobi, Kenya.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ability to transfer information about the performance, safety, and environmental impacts of a genetically modified (GM) crop from confined field trials (CFTs) conducted in one location to another is increasingly gaining importance in biosafety regulatory assessment and decision-making. The CFT process can be expensive, time-consuming, and logistically challenging. Data transportability can help overcome these challenges by allowing the use of data obtained from CFTs conducted in one country to inform regulatory decision-making in another country. Applicability of transported CFT data would be particularly beneficial to the public sector product developers and small enterprises that develop innovative GM events but cannot afford to replicate redundant CFTs, as well as regulatory authorities seeking to improve the deployment of limited resources. This review investigates case studies where transported CFT data have successfully been applied in biosafety assessment and decision-making, with an outlook of how African countries could benefit from a similar approach.

Indexed as

Crops, AgriculturalPlants, Genetically ModifiedAfricaAgricultureDecision MakingHumansbiosafety regulatoryCFT datadecision-makingfield trialsGM cropstransportability

Identifiers

PMID38980826
PMCPMC11236291

What OpenQuestion 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 OpenQuestion graph.