Creatine supplementation combined with resistance training in older men
Bibliographic record
Abstract
PURPOSE: To study the effect of creatine (Cr) supplementation combined with resistance training on muscular performance and body composition in older men. METHODS: Thirty men were randomized to receive creatine supplementation (CRE, N = 16, age = 70.4 +/- 1.6 yr) or placebo (PLA, N = 14, age = 71.1 +/- 1.8 yr), using a double blind procedure. Cr supplementation consisted of 0.3-g Cr.kg(-1) body weight for the first 5 d (loading phase) and 0.07-g Cr.kg(-1) body weight thereafter. Both groups participated in resistance training (36 sessions, 3 times per week, 3 sets of 10 repetitions, 12 exercises). Muscular strength was assessed by 1-repetition maximum (1-RM) for leg press (LP), knee extension (KE), and bench press (BP). Muscular endurance was assessed by the maximum number of repetitions over 3 sets (separated by 1-min rest intervals) at an intensity corresponding to 70% baseline 1-RM for BP and 80% baseline 1-RM for the KE and LP. Average power (AP) was assessed using a Biodex isokinetic knee extension/flexion exercise (3 sets of 10 repetitions at 60 degrees.s(-1) separated by 1-min rest). Lean tissue (LTM) and fat mass were assessed using dual energy x-ray absorptiometry. RESULTS: Compared with PLA, the CRE group had significantly greater increases in LTM (CRE, +3.3 kg; PLA, +1.3 kg), LP 1-RM (CRE, +50.1 kg; PLA +31.3 kg), KE 1-RM (CRE, +14.9 kg; PLA, +10.7 kg), LP endurance (CRE, +47 reps; PLA, +32 reps), KE endurance (CRE, +21 reps; PLA +14 reps), and AP (CRE, +26.7 W; PLA, +18 W). Changes in fat mass, fat percentage, BP 1-RM, and BP endurance were similar between groups. CONCLUSION: Creatine supplementation, when combined with resistance training, increases lean tissue mass and improves leg strength, endurance, and average power in men of mean age 70 yr.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".