Evidence map›Paper›PMID 28926963›Full record

SynthesisNutrients2017

Broad and Inconsistent Muscle Food Classification Is Problematic for Dietary Guidance in the U.S.

Cody L Gifford, Lauren E O'Connor, Wayne W Campbell, Dale R Woerner, Keith E Belk

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in Nutrients, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
1.2field-weighted citation impact, top 18% 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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 20 citations in OpenAlex.

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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

5 authors at 2 institutions in 1 country.

Cody L GiffordDepartment of Animal Science, Colorado State University, Fort Collins, CO 80523, USA. cody.gifford@rams.colostate.edu.ORCID 0000-0003-2023-0228
Lauren E O'ConnorDepartment of Nutrition Science, Purdue University, West Lafayette, IN 47907, USA. leoconno@purdue.edu.
Wayne W CampbellDepartment of Nutrition Science, Purdue University, West Lafayette, IN 47907, USA. campbellw@purdue.edu.
Dale R WoernerDepartment of Animal Science, Colorado State University, Fort Collins, CO 80523, USA. dale.woerner@colostate.edu.
Keith E BelkDepartment of Animal Science, Colorado State University, Fort Collins, CO 80523, USA. keith.belk@colostate.edu.
Colorado State University · USPurdue University West Lafayette · US

Funding

UNIV OF NORTH CAROLINA CLINICAL NUTRITION RESEARCH UNITP30DK056350 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Ian Michael Carroll · 1999 to 2026
$31.6M
Interdisciplinary Training in Signals Controlling Ingestion and ObesityT32DK076540 · NIDDK · PURDUE UNIVERSITY · PI MATTES, RICHARD D · 2008 to 2018
$766k
NIDDK NIH HHS P30 DK056350NIDDK NIH HHS T32 DK076540
6 · The paper itself

Abstract

Dietary recommendations regarding consumption of muscle foods, such as red meat, processed meat, poultry or fish, largely rely on current dietary intake assessment methods. This narrative review summarizes how U.S. intake values for various types of muscle foods are grouped and estimated via methods that include: (1) food frequency questionnaires; (2) food disappearance data from the U.S. Department of Agriculture Economic Research Service; and (3) dietary recall information from the National Health and Nutrition Examination Survey data. These reported methods inconsistently classify muscle foods into groups, such as those previously listed, which creates discrepancies in estimated intakes. Researchers who classify muscle foods into these groups do not consistently considered nutrient content, in turn leading to implications of scientific conclusions and dietary recommendations. Consequentially, these factors demonstrate a need for a more universal muscle food classification system. Further specification to this system would improve accuracy and precision in which researchers can classify muscle foods in nutrition research. Future multidisciplinary collaboration is needed to develop a new classification system via systematic review protocol of current literature.

Indexed as

Nutrition AssessmentNutrition PolicyAnimalsDietFishesHumansMeatMeat ProductsMeta-Analysis as TopicNutrition SurveysNutritive ValuePoultryRed MeatSeafoodUnited Statesassessment methodsclassificationdietary recommendationsmuscle foodsnutrient contentspecification

Identifiers

PMID28926963
PMCPMC5622787
OpenAlexW2754787669

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.