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Record W1486865484 · doi:10.1159/000329288

Effect of Protein, Dairy Components and Energy Balance in Optimizing Body Composition

2011· review· en· W1486865484 on OpenAlexaff
Stuart M. Phillips, Michael B. Zemel

Bibliographic record

VenueNestlé Nutrition Institute Workshop series · 2011
Typereview
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDietingLean body massWeight lossFat massComposition (language)EndocrinologyFood scienceBasal metabolic rateEnergy expenditureChemistryBody weightInternal medicineAnimal scienceBiologyObesityMedicine

Abstract

fetched live from OpenAlex

Weight loss is achieved through the consumption of a hypoenergetic diet and/or increased energy expenditure through exercise. While weight loss is associated with numerous benefits, the pattern of weight loss in terms of body composition changes is not always studied. In our view, the optimum pattern of weight loss is one in which fat mass is lost and lean mass is preserved. The preservation of lean mass has important consequences due to the role of this tissue in contributing to basal metabolic rate, controlling glycemia, and contributing to lipid oxidation. We also propose that a preservation of lean mass would have important consequences in resisting weight regain after loss. We review dietary practices, including reduced consumption of dietary carbohydrate, consuming higher than recommended dietary protein, with an emphasis on dairy sources, as well as dietary calcium, to accelerate the loss of fat mass during dieting and preserve lean mass. Available evidence suggests that each practice has a highly plausible mechanistic and growing clinical rationale in terms of efficacy in promoting fat mass loss and lean mass retention during a hypoenergetic diet.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.907
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
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 head0.033
GPT teacher head0.305
Teacher spread0.271 · 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.

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

Citations13
Published2011
Admission routes1
Has abstractyes

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