Bibliographic record
Abstract
OBJECTIVE: : To evaluate the evidence for dietary recommendations in older adult athletes. DESIGN: : Interpretive review of the literature. RESULTS: : Regarding resistance training, a protein intake of slightly more than 0.8 g/kg/d is required to optimize gains in muscle strength. The early provision of protein and carbohydrate following a weight training session can enhance resultant strength and fat-free mass gains. Supplementation with creatine monohydrate (approximately 5 g/d) can potentiate some of the gains in strength and fat free mass attained through resistance exercise training. Regarding endurance exercise training, there are no studies evaluating carbohydrate loading, during-event, or postexercise carbohydrate/nutritional replacement in older adults. CONCLUSIONS: : The amount and timing of dietary protein is important to maximize strength and gains in fat-free mass during resistance exercise training. Creatine monohydrate supplementation can potentiate some of these gains during the first 4 to 6 months of training. Older adults should consume adequate carbohydrates during endurance training (6-8 g/kg/d) and may benefit from the provision of carbohydrate and protein in the early recovery phase following endurance exercise to maximize glycogen re-synthesis for a subsequent exercise bout. There is no scientific reason to assume that older athletes will respond differently to the pre- and during-race fluid and carbohydrate replacement strategies suggested for younger athletes. The consensus guidelines outlined by the American College of Sports Medicine should therefore be followed for all athletes, regardless of their age.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".