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Record W4387406669 · doi:10.1007/s12603-023-1995-9

Solving the Obesity Crisis in Older Adults with the Mediterranean Diet: Policy Brief

2023· article· en· W4387406669 on OpenAlexaff
Andrew M. R. Hanna

Bibliographic record

VenueThe journal of nutrition health & aging · 2023
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsQueen's University
Fundersnot available
KeywordsObesityMediterranean dietMedicineGerontologyDiseaseEnvironmental healthPsychological interventionPopulationPopulation ageingDemographyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Obesity is a chronic disease classified by excessive accumulation of fat which may impair health. The prevalence of obesity is increasing in most nations worldwide, both developed and developing. At the same time, the aging population is also growing worldwide. In the United States, approximately 38% of adults 60+ years old are obese, with similar trends in Canada and the United Kingdom. Obesity is associated with increased risk of death (mortality) and disease (morbidity) and carries specific risks for older adults, such disability and frailty. It also presents a financial burden. The Mediterranean Diet (MedDiet) is an extensively studied healthy diet pattern which can be used to combat obesity in older populations. Specifically for older adults, the MedDiet has benefits over other common diets or weight-loss interventions. This policy brief provides suggestions specifically for the Canadian population, though they are general enough to be applied to other countries.

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)0.014
metaresearch head score (Gemma)0.056
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 score0.042
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.056
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0050.003
Scholarly communication0.0110.010
Open science0.0020.009
Research integrity0.0420.031
Insufficient payload (model declined to judge)0.0180.002

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 head0.026
GPT teacher head0.323
Teacher spread0.297 · 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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractno

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