Milk consumption after resistance exercise increases fat loss and increases muscle mass and strength gains in young women
Bibliographic record
Abstract
Post‐resistance exercise consumption of fat free milk in men promoted greater lean mass accretion and fat mass loss than soy or carbohydrate. It is unknown if similar body compositional changes and strength gains occur in women. Twenty young women were randomized to drink 500ml of fat‐free milk (MILK: n=10; BMI=26.2±2.0kg/m 2 ; mean±SE) or carbohydrate (CON: n=10; BMI=25.6±1kg/m 2 ) on 2 occasions; immediately post and 1h post‐exercise. Subjects underwent resistance training 5d/wk for 12‐wk. Body compositional changes were measured by DXA, and subjects' strength was measured by 1RM pre‐ and post‐training. Baseline body mass did not differ between groups and only CON gained weight after training (CON: 68.3±4.1kg to 69.1±4.0kg, P=0.05; MILK: 72.0±4.1kg to 72.5±3.8kg, P=0.24). Lean mass increased with training in both groups (P< 0.01), with a greater net change in MILK vs CON (1.9±0.2kg vs 1.1±0.2kg, respectively; P<0.01). Fat mass decreased with training in MILK only (‐1.64±0.4kg, P<0.01) with no change in CON (‐0.27±0.3kg, P=0.47). Bench press and tricep push‐down strength increased more in MILK than CON (P<0.05). Thus, post‐exercise consumption of milk vs isoenergetic carbohydrate resulted in greater muscle mass accretion, fat mass loss and strength gains in women after 12‐wk of resistance training. Our results parallel those shown previously in men. Sponsored by NSERC, CIHR and The Dairy Farmers of Canada Grant Funding Source CIHR
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".