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Record W2074399615 · doi:10.1080/07315724.2013.816604

Dairy in Adulthood: From Foods to Nutrient Interactions on Bone and Skeletal Muscle Health

2013· review· en· W2074399615 on OpenAlexaff
Jean‐Philippe Bonjour, Marius Kraenzlin, Régis Levasseur, Michelle P. Warren, Susan J. Whiting

Bibliographic record

VenueJournal of the American College of Nutrition · 2013
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSarcopeniaEndocrinologyVitamin D and neurologyInternal medicineOsteoporosisBone remodelingBone resorptionSkeletal muscleMedicineDietary Reference IntakeNutrientChemistry

Abstract

fetched live from OpenAlex

The risk of fragility fractures exponentially increases with aging. Reduced mass and strength of both bone in osteoporosis and skeletal muscle in sarcopenia play a key role in the age-related incidence of fragility fractures. Undernutrition is often observed in the elderly, particularly in those subjects experiencing osteoporotic fractures, more likely as a cause than a consequence. Calcium (Ca), inorganic phosphate (Pi), vitamin D, and protein are nutrients that impact bone and skeletal muscle integrity. Deficiency in the supply of these nutrients increases with aging. Dairy foods are rich in Ca, Pi, and proteins and in many countries are fortified with vitamin D. Dairy foods are important souces of these nutrients and go a long way to meeting the recommendations, which increase with aging. This review emphaszes the interactions between these 4 nutrients, which, along with physical activity, act through cellular and physiological pathways favoring the maintenance of both bone and skeletal muscle structure and function.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.901
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.051
GPT teacher head0.388
Teacher spread0.337 · 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 designNot applicable
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

Citations90
Published2013
Admission routes1
Has abstractyes

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