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Record W4401906958 · doi:10.1097/nt.0000000000000698

Let’s Try This

2024· article· en· W4401906958 on OpenAlexaboutno aff
Marie E. Latulippe

Bibliographic record

VenueNutrition Today · 2024
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsnot available
Fundersnot available
KeywordsTransparency (behavior)Advisory committeeHuman servicesEquity (law)Variety (cybernetics)Inclusion (mineral)Diversity (politics)Food groupPsychologyPublic relationsEnvironmental healthMedicineMedical educationPolitical scienceComputer science

Abstract

fetched live from OpenAlex

The 2025-2030 Dietary Guidelines Advisory Committee (DGAC)1 is well into their evaluation of the scientific underpinnings to support the next iteration of the Dietary Guidelines for Americans. The guidelines inform a variety of federal nutrition assistance programs including the National School Lunch Program and the food packages for Women, Infants and Children, among others, and provide guidance across sectors for messaging, food innovation, and public policy. The DGAC, which is comprised of academic experts in fields critical to the guidelines, spends several years following a transparent process that will eventually deliver a report to the secretaries of the US Department of Agriculture and Department of Health & Human Services for their use in updating the Dietary Guidelines for Americans policy document. For those who have attended the meetings, it is clear that the 2025-2030 advisory process is adhering to transparency about decision-making, is substantially advancing methods for dietary pattern analysis, and continues to incorporate diversity, equity, and inclusion across the 3 streams of work.2 Yet, for a few of the topics at hand, the committee is not reaching conclusions—limited, moderate, or strong—either for lack of peer-reviewed evidence or variability in published research methods across the literature base. Those areas include the relationship between the number of eating occasions and weight, and consumption during pregnancy of low- and no-calorie sweetened beverages and gestational weight gain, among others. In several previous and the current round of evidence review, the DGAC turned to an evidence synthesis approach called “systematic review” to address the relationship between dietary patterns and cognitive outcomes. For these reviews, the DGAC crafts very specific questions that are addressed through a literature search, using the using the PICO (population, intervention, comparator, outcome) approach. In 2020, one of the DGAC questions was: “What is the relationship between dietary patterns consumed and neurocognitive health?”3 The query focused on the outcomes of cognitive decline, mild cognitive impairment, dementia, and Alzheimer’s disease. In the 2020 final report,4 the DGAC noted that in addressing this question, it faced a literature having “considerable variation in testing methods, inconsistent validity and reliability of cognitive testing methods, and differences between dietary patterns and cognitive outcomes examined.” All of this constrained their ability to draw conclusions. Interestingly, the 2025-2030 DGAC has decided to pursue a similar topic, rephrasing the question slightly: “What is the relationship between dietary patterns and risk of cognitive decline, dementia, and Alzheimer’s disease?”5 The challenge faced since 2020 is a long-standing complication. With nutrition and cognition research, the variability identified due to cognitive performance test selection and administration hinders progress—a challenge revisited about every 5 years in the literature in some form of review. It is likely that the 2020 obstacles related to dietary patterns and neurocognitive outcomes will persist as the current DGAC tackles this question. In 2023, the Institute for the Advancement of Food and Nutrition Sciences (IAFNS) organized a careful, salient review6 to understand where to go next. What do experts agree are the challenges? How can they be addressed? This may allow the field to move past this obstacle to offering the public dietary guidance for cognitive support. The reality is that one cannot ask the psychology research community to reduce variability by aligning their methods—particularly test selection—when there are many good cognitive performance tests from which to choose. However, the variety of cognitive tests available poses challenges to looking across studies and synthesizing evidence to support the development of science-based nutrition guidance. In response to this challenge, an International Expert Group7 was formed by IAFNS to innovate ways around this conundrum. The IAFNS Expert Group proposed that Retrospective Harmonization, an approach spearheaded by the Maelstrom Research Institute at McGill University,8 could be a way to leverage data from long-term prospective cohort studies that measure dietary intake and cognitive performance. It would align dietary patterns across the studies and evaluate cognitive outcomes based on aligned metrics. The idea is that by pooling individual-level data, the strength or weakness of the association becomes clearer. This Expert Group spent about a year developing the innovative methodology, now published,9 and over the course of 2024 will perform the analysis. Throughout this process, the Expert Group is also gaining a deep understanding of the nuanced differences in large study implementation (for example, various ways of collecting and reporting dietary information) that make it challenging to compare results—and usually we make comparisons based on the published literature, which can gloss over these details. Will this resolve the challenge? Will all questions about dietary patterns and cognitive outcomes be addressed and produce conclusive evidence as to which pattern is best? That is probably ambitious, but the IAFNS Expert Group has produced a pivotal building block with the potential to make progress against this apparent impasse. By trying a new approach with existing data, IAFNS and its collaborators are evaluating an innovative way around the barriers posed by the variability in available cognitive measures. Once the analysis is complete, IAFNS hopes to see new patterns of evidence that shed light on dietary guidance and cognitive support. As a scientific community, the more energy directed toward innovation and collaboration, the better the field can address today’s nutrition science challenges to advance public health.

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)0.017
metaresearch head score (Gemma)0.082
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.159
Threshold uncertainty score0.532

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.082
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.002
Science and technology studies0.0060.009
Scholarly communication0.0160.027
Open science0.0040.010
Research integrity0.0160.024
Insufficient payload (model declined to judge)0.1590.092

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 head0.021
GPT teacher head0.290
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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