Effects of Creatine and Resistance Training on Bone Health in Postmenopausal Women
Bibliographic record
Abstract
PURPOSE: Our primary purpose was to determine the effect of 12 months of creatine (Cr) supplementation during a supervised resistance training program on properties of bone in postmenopausal women. METHODS: Participants were randomized (double-blind) into two groups: resistance training (3 d·wk) and Cr supplementation (0.1 g·kg·d) or resistance training and placebo (Pl). Our primary outcome measures were lumbar spine and femoral neck bone mineral density (BMD). Secondary outcome measures were total hip and whole-body BMD, bone geometric properties at the hip, speed of sound at the distal radius and tibia, whole-body lean tissue mass, muscle thickness, and bench press and hack squat strength. Forty-seven women (57 (SD, 6) yr; Cr, n = 23; Pl, n = 24) were randomized, with 33 analyzed after 12 months (Cr, n = 15; Pl, n = 18). RESULTS: Cr attenuated the rate of femoral neck BMD loss (-1.2%; absolute change (95% confidence interval), -0.01 (-0.025 to 0.005) g·cm) compared with Pl (-3.9%; -0.03 (-0.044 to -0.017) g·cm; P < 0.05) and also increased femoral shaft subperiosteal width, a predictor of bone bending strength (Cr, 0.04 (-0.09 to 0.16) cm); Pl, -0.12 (-0.23 to -0.01) cm; P < 0.05). Cr increased relative bench press strength more than Pl (64% vs 34%; P < 0.05). There were no differences between groups for other outcome measures. There were no differences between groups for reports of serum liver enzyme abnormalities, and creatinine clearance was normal for Cr participants throughout the intervention. CONCLUSIONS: Twelve months of Cr supplementation during a resistance training program preserves femoral neck BMD and increases femoral shaft superiosteal width, a predictor of bone bending strength, in postmenopausal women.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".