Effects of Carbohydrate-Protein Ingestion Post-Resistance Training in Male Rugby Players
Bibliographic record
Abstract
Evidence suggests that carbohydrate-protein (CHO-PRO) drinks post-exercise are an advantageous nutritional recovery intervention. Resistance trained (n = 14, mean ± SD; age 19 ± 1 yr, mass 95 ± 9 kg, % fat 17 ± 4 % and BMI 28.5 ± 1.8 kg.m-2) male rugby players participated in a study investigating effects of carbohydrate (CHO) and CHO-PRO drinks on subsequent resistance exercise performance. Following an initial resistance training (RT) protocol consisting of 8 circuits of 5 discrete exercises at 10 repetition maximum (RM), participants received 10 mL.kg-1 BM of randomised sports drink (LCHO, HCHO and CHO-PRO) on completion of the RT protocol and at 120 min into a 240 min recovery period. Post-recovery, participants completed a test to failure (TTF) protocol performing as many circuits of the same exercises at 10-RM to failure. Individual exercise cumulative load (ƩW) lifted and total work capacity (TWC) for each trial was recorded. Both ƩW and TWC were normalised for body mass (kg.kg-1 BM). Data were analysed using repeated measures ANOVA with post-hoc Student-Neuman-Keuls pair-wise comparisons (P<0.05). Despite large intra-subject variability between trials, TWC normalised for body mass was significantly greater following CHO-PRO compared with HCHO and LCHO (188 ± 26 vs. 157 ± 21 and 150 ± 16 kg.kg-1 BM, respectively; P<0.05). The ƩW lifted after ingestion of HCHO and LCHO were not significantly different despite differing CHO and caloric content. The CHO-PRO induced enhancement of recovery was possibly due to higher rates of glycogen restoration after the initial glycogen depleting RT protocol.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".