Effects of Creatine Supplementation and Progressive Resistance Training in Stroke Survivors
Bibliographic record
Abstract
International Journal of Exercise Science 15(2): 1117-1132, 2022. The purpose was to investigate the effects of progressive resistance training (PRT) and creatine supplementation in stroke survivors. Participants were randomized to one of two groups: creatine (n = 5; 51 ± 16y) or placebo (n = 3; 73 ± 8y) during 10 weeks of supervised PRT. Prior to and following PRT and supplementation, assessments were made for body composition (lean tissue and fat mass), muscle thickness, muscle strength (1-repetition maximum), functional exercise capacity (6-minute walk test, Berg Balance Scale; BBS), cognition (Montreal Cognitive Assessment; MoCA), and symptoms of anxiety (Generalized Anxiety Disorder Assessment-7; GAD-7) and depression (Center for Epidemiological Studies Depression Scale; CES-D). There were time main effects for leg press strength (increased; p = 0.001), chest press strength (increased; p = 0.003), elbow flexor muscle thickness (increased; p = 0.007), BBS (increased; p = 0.002), MoCA (increased; p = 0.031) and CES-D (decreased; p = 0.045). There was a group x time interaction for the 6 minute walk test (p = 0.039). The creatine group significantly increased walking distance over time (p = 0.002) with no change in the placebo group (p = 0.120). Ten weeks of PRT had some positive effects on measures of muscle strength and size, balance, cognition and depression. The addition of creatine to PRT significantly improved walking performance in stroke survivors.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".