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EFFECT OF CREATINE SUPPLEMENTATION COMBINED WITH RESISTANCE TRAINING ON WHOLE BODY BONE MINERAL IN OLDER MEN

2001· article· en· W2001689719 on OpenAlexaff
Murray Justin Chrusch, Philip D. Chilibeck, K E. Chad, Kelly Davison, Darren Burke

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBone mineralCreatineCreatine MonohydrateBone mineral contentMedicinePlaceboResistance trainingBody weightInternal medicineDual energyDual-energy X-ray absorptiometryEndocrinologyBone massAnimal sciencePhysical therapyOsteoporosisBiology

Abstract

fetched live from OpenAlex

The objective of the study was to determine the effect of creatine supplementation combined with resistance training on bone mass of older men. Twenty-nine men were randomized to receive creatine supplementation (CR, n = 16, age = 70.4y) or placebo (PL, n = 13, age = 71.6y) using a double blind procedure. Cr supplementation consisted of 0.3 g/kg body weight for 5 days and 0.07 g/kg body weight thereafter. Both groups participated in whole-body resistance training (12 weeks, 3x/wk, 3 sets of 10 reps, 12 exercises). Bone mass was assessed by dual energy x-ray absorptiometry. There was a time main effect for whole body bone mineral density, with each group increasing by 0.5% (p = 0.05). There was a group × time interaction for bone mineral content of the arms (p = 0.0005), with the Cr group increasing by 3.2% (p = 0.0009) and the PL group decreasing by 1.0% (not significant). We conclude that 12 weeks of resistance training increases whole body bone mineral density and that creatine supplementation provides a further increase in bone mineral content. This study was supported by MuscleTech Research and Development Inc.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.270
Teacher spread0.263 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations31
Published2001
Admission routes1
Has abstractyes

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