Dietary analysis of full marathon runners over 50 years of age: a retrospective study.
Bibliographic record
Abstract
AIM: There is little information known about the dietary intakes of older athletes. The purpose of this study was to assess the dietary intakes of full marathon runners over the age of 50 during the 3 days prior to and during a 26.2 mile marathon. METHODS: Participants completed a 3-day food record prior to the marathon and a food recall post-marathon. Dietary records were entered into a nutrient analysis software program and were analyzed for total energy, macronutrients, and micronutrients. RESULTS: The average energy intake based on the 3-day food records was 2670±225 kcal/day, which was below the estimated energy requirement of 3140±102 kcal/day. The participants consumed 56%, 17% and 27% of energy from carbohydrates, protein and fat, respectively, and these were within the acceptable macronutrient distribution ranges. The participants had a favorable omega-6 to omega-3 ratio of 5:1. The participants were consuming 3920±350 mg/day of sodium which is 70% above the goal of 2300 mg/day. The participants met the dietary reference intakes for only half of the micronutrients. Based on the food recall, 87% of the participants' energy intake was from carbohydrates (213±19 g or 852±75 kcal) before and during the marathon. CONCLUSION: Marathon runners over the age of 50 have a balanced diet during the 3 days prior to running a 26.2 mile marathon. Carbohydrates were the main source of energy consumed before and during the marathon.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".