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Record W2099386773 · doi:10.2903/sp.efsa.2012.en-367

Implementation of systematic reviews in EFSA scientific outputs workflow

2012· article· en· W2099386773 on OpenAlexaff
Annette M. O’Connor, Gábor L. Löveï, Jacqualyn Eales, Geoff K Frampton, Julie Glanville, Andrew S. Pullin, Jan M. Sargeant

Bibliographic record

VenueEFSA Supporting Publications · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural safety and regulations
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsLibrary scienceComputer science

Abstract

fetched live from OpenAlex

Systematic reviews (SR) are an evidence synthesis approach that provides robust and transparent answers to clearly formulated questions. Originally developed for use in clinical practice, SRs have wider applicability, including food and feed safety risk assessment. EFSA has implemented the use of SRs, and this document contributes to the further development of this in-house capacity. Since the publication of the document “Application of Systematic Review Methodology to Food and Feed Safety Assessments to Support Decision Making”, which mainly focuses on interventions and exposures (PECO/PICO), little has changed in this arena. Fast increasing fields of application include chemical and environmental risk assessment, and analysing environmental management interventions. Considering time constraints at EFSA, the use of SRs should be pursued thoughtfully. Important are the use of explicit systematic methods aimed at minimising bias and maximising transparency in order to produce the most reliable findings that can be used to inform decision making. Participants of the training courses indicated SRs should be a priority for controversial topics (which might be subject to greater scrutiny by external parties, including the public, and thereby would benefit from maximum transparency) or topics for which there was disagreement amongst experts. Some areas addressed by EFSA have considerable potential impact, for example related to public health or animal trade, and these topics could be prioritised for SR. Under severe time constraints, a full SR may not be possible, but a rapid review can be considered. However rapid reviews are not a substitute for systematic reviews. Adoption of rapid reviews exchanges one set of concerns (time and resources contracts) for another (lack of robustness and comprehensiveness). In the view of the Consortium, the continuation of training opportunities is important. Appropriate commissioning of SR expertise is an important step in establishing the role of the methodology in EFSA risk assessments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.580
metaresearch head score (Gemma)0.723
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.420
Threshold uncertainty score0.518

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5800.723
Meta-epidemiology (narrow)0.0040.009
Meta-epidemiology (broad)0.0110.017
Bibliometrics0.0470.045
Science and technology studies0.0060.005
Scholarly communication0.0340.019
Open science0.0110.031
Research integrity0.0090.013
Insufficient payload (model declined to judge)0.1040.068

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.075
GPT teacher head0.323
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2012
Admission routes1
Has abstractyes

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