Creatine Supplementation For Weak Muscles In Persons With Chronic Tetraplegia: A Randomized Double-Blind Placebo-Controlled Crossover Trial
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Creatine supplementation improves muscle strength in some patient populations with neurologic disorders. The purpose of this study was to determine whether creatine supplementation improves muscle strength and endurance in weak upper limb muscles in persons with tetraplegia, and whether it improves function. METHODS: Outpatients with tetraplegia and mild wrist extensor weakness were randomized to receive either creatine or placebo in a double-blind crossover design. During creatine supplementation, participants were loaded with 10 g orally twice per day for 6 days, then maintained on 5 g daily until undergoing testing. Main outcome measures, performed at baseline, after placebo, and after creatine supplementation, included maximal voluntary wrist extensor isometric contraction strength (MVC), endurance times for 5 submaximal wrist extensor contractions, and the Grasp and Release Test for hand function. RESULTS: Eight individuals (7 men, 1 woman) with tetraplegia met inclusion criteria and completed all study phases. The mean age of participants was 48 years, and 7 of 8 had C6 motor level injuries. There were no significant differences in MVC, endurance times, or hand function for creatine vs placebo. CONCLUSION: Creatine does not improve MVC and endurance of weak wrist extensors and does not improve hand function in individuals with tetraplegia.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".