The potential of creatine monohydrate supplementation in the management of osteosarcopenia
Bibliographic record
Abstract
PURPOSE OF REVIEW: Osteosarcopenia is an age-related condition characterized by reductions in bone mineral, muscle/lean mass, strength and functional ability which increases the risk of falls, fractures, frailty and premature mortality. One main contributing factor to osteosarcopenia is malnutrition. The purpose was to review recent evidence of creatine monohydrate (CrM) supplementation in older adults and to discuss the potential to manage osteosarcopenia. RECENT FINDINGS: Accumulating research shows that CrM supplementation, primarily when combined with exercise training, has the potential to serve as a viable intervention in the management of osteosarcopenia. Collectively, CrM supplementation during exercise training in older adults led to greater improvements in whole-body lean mass, lower-limb muscle density and bone geometry and muscle strength (primarily upper-body) compared to exercise training alone. However, no study has investigated the effects of CrM, with and without exercise training, in older adults with osteosarcopenia. SUMMARY: Given the positive findings of CrM on measures of muscle and bone in healthy older adults, there is potential for CrM to be added as an adjunct to exercise training in the management of osteosarcopenia. However, randomized clinical trials are needed to confirm the safety and efficacy of this nutrient in this clinical population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".