MétaCan
Menu
← Back to cohort
Record W2030361975 · doi:10.1007/s12603-009-0224-5

Potential of creatine supplementation for improving aging bone health

2010· review· en· W2030361975 on OpenAlexaff
Darren G. Candow, Philip D. Chilibeck

Bibliographic record

VenueThe journal of nutrition health & aging · 2010
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of SaskatchewanUniversity of Regina
Fundersnot available
KeywordsResistance trainingCreatineBone resorptionBone healthMedicineOsteoporosisStrength trainingResorptionPhysiologyInternal medicineEndocrinologyBone mineral

Abstract

fetched live from OpenAlex

Aging subsequently results in bone and muscle loss which has a negative effect on strength, agility, and balance leading to increased risks of falls, injuries, and fractures. Resistance training is an effective strategy for maintaining bone mass, possibly by increasing activity of cells involved in bone formation and reducing activity of cells involved in bone resorption. However, bone loss is still evident in older adults who have maintained resistance training for most of their life, suggesting that other factors such as nutrition may be involved in the aging bone process. Emerging evidence suggests that creatine supplementation, with and without resistance training, has the potential to influence bone biology. However, research investigating the longer-term effects of creatine supplementation and resistance training on aging bone is limited.

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)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.027
GPT teacher head0.367
Teacher spread0.341 · 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
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

Citations21
Published2010
Admission routes1
Has abstractno

Explore more

Same venueThe journal of nutrition health & aging→Same topicMuscle metabolism and nutrition→French-language works237,207→