Effect of Whey and Soy Protein Supplementation Combined with Resistance Training in Young Adults
Bibliographic record
Abstract
The purpose was to compare changes in lean tissue mass, strength, and myofibrillar protein catabolism resulting from combining whey protein or soy protein with resistance training. Twenty-seven untrained healthy subjects (18 female, 9 male) age 18 to 35 y were randomly assigned (double blind) to supplement with whey protein (W; 1.2 g/kg body mass whey protein + 0.3 g/kg body mass sucrose power, N = 9: 6 female, 3 male), soy protein (S; 1.2 g/kg body mass soy protein + 0.3 g/kg body mass sucrose powder, N= 9: 6 female, 3 male) or placebo (P; 1.2 g/kg body mass maltodextrine + 0.3 g/kg body mass sucrose powder, N = 9: 6 female, 3 male) for 6 wk. Before and after training, measurements were taken for lean tissue mass (dual energy X-ray absorptiometry), strength (1-RM for bench press and hack squat), and an indicator of myofibrillar protein catabolism (urinary 3-methylhistidine). Results showed that protein supplementation during resistance training, independent of source, increased lean tissue mass and strength over isocaloric placebo and resistance training (P < 0.05). We conclude that young adults who supplement with protein during a structured resistance training program experience minimal beneficial effects in lean tissue mass and strength.
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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 | 0.000 |
| 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".