Evaluation of Dietary Intakes and Supplement Use in Paralympic Athletes
Bibliographic record
Abstract
Dietary intakes and supplement use in Paralympic athletes remains largely unexplored, and specialized recommendations are lacking. The aim of this study was to evaluate nutrient intakes and supplement use in high-performance athletes with physical disabilities using three-day food records and a validated dietary supplement use questionnaire. A secondary aim examined gender differences in nutrient and supplement intakes. Male (n = 18) and female (n = 22) athletes were recruited from nine Paralympic sports through sporting organizations, coaches, and social media. Athletes generally met able-bodied recommendations for macronutrients. Male and female athletes often failed to meet the Recommended Dietary Allowance (RDA) or Adequate Intake (AI) for vitamin D, vitamin E, pantothenic acid, magnesium, and potassium. On average, females did not meet the RDA for iron and calcium, whereas males did not meet the RDA for vitamin A and folate. Commonly consumed supplements were vitamin D, protein powder, sport bars, and sport drinks. Analysis of diet and supplement use within this population shows several micronutrient deficiencies and irregular use of specific supplements. Athlete support and education is required to optimize nutrition in Paralympic athletes.
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 | 0.001 | 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".