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Evidence Collection and Evaluation for the Development of Dietary Guidelines and Public Policy on Nutrition

2019· review· en· W2969635699 on OpenAlexaff
Dena Zeraatkar, Bradley C. Johnston, Gordon Guyatt

Bibliographic record

VenueAnnual Review of Nutrition · 2019
Typereview
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsDalhousie UniversityMcMaster UniversityImpact
FundersAustralian GovernmentU.S. Department of AgricultureU.S. Department of Health and Human Services
KeywordsGuidelineGrading (engineering)Quality of evidenceSystematic reviewEvidence-based medicineGovernment (linguistics)MedicineEvidence-based policyEvidence-based practiceCertaintyRisk analysis (engineering)MEDLINEManagement sciencePolitical scienceMeta-analysisAlternative medicineEconomicsEngineeringPathology

Abstract

fetched live from OpenAlex

Dietary guidelines and recommendations, usually developed by government bodies or large authoritative organizations, have major downstream effects on public policy. A growing body of evidence supports the notion that there are serious deficiencies in the methods used to develop dietary guidelines. Such deficiencies include the failure to access or conduct comprehensive systematic reviews, a lack of systematic or rigorous evaluation of the quality of the evidence, a failure to acknowledge the limitations of the evidence base underlying recommendations, and insufficiently stringent management of conflicts of interest. These issues may be addressed by adhering to international standards for guideline development, including adopting systematic review methodology and using rigorous systems to evaluate the certainty of the evidence and to move from evidence to recommendations, of which the GRADE approach (Grading of Recommendations Assessment,Development and Evaluation) is the most rigorous and fully developed. Improving the methods by which dietary guidelines are produced has considerable potential to substantially improve public policy decision-making.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.637
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.343
GPT teacher head0.488
Teacher spread0.145 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations35
Published2019
Admission routes1
Has abstractyes

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