Creatine Monohydrate Enhanced Fixed and Planned Load Reduction Resistance Training without Altering Ratings of Perceived Exertion
Bibliographic record
Abstract
Creatine enhances resistance training adaptations and may alter ratings of perceived exertion (RPE). Planned load reduction training (10% reduction in load per set) lowered RPE compared to traditional fixed load resistance training (3 sets at a constant load) with similar muscle adaptations. The purpose was to examine the effects of creatine on performance and RPE during both traditional fixed and planned load reduction training compared to placebo. Forty resistance trained males were randomly assigned to either creatine (20 g∙day-1) or placebo for 7 days. Following the loading phase, all participants completed 3 resistance training protocols (3 sets of bench press and smith machine squats) in random order; a constant load (CON), 5% load reduction each set (RED 5), and a 10% load reduction each set (RED 10). Total repetitions and RPE were recorded each set. Creatine supplementation increased bench press repetitions with no significant difference in RPE compared to placebo. There were no other differences between supplements or protocols. Creatine supplementation was able to augment bench press performance compared to placebo without increasing RPE. Creatine did not differentially influence fixed compared to planned load reduction training nor was it able to enhance lower body squat total repetitions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".