NUTRIENT INTAKE OF ELITE ATHLETES WITH SPINAL CORD INJURY
Bibliographic record
Abstract
H. Gerrish1, K. Pritchett1, D. Ogan1; R. Pritchett1; M. LaCroix2; E. Broad2 1Central Washington University, Ellensburg, WA, 2US Olympic Committee, Chula Vista, CA The nutrient needs of athletes with spinal cord injury (SCI) are dependent on their physiological alterations, training load, and intensity of practice. Limited research is available regarding the current nutrient intake and geographical consumption patterns of elite SCI athletes. PURPOSE: This study examined the diets of Canadian (CAN) and American (USA) elite athletes with SCI from the United States Paralympic and Canadian Sport Institute programs utilizing a self-reported 24-hour diet recall. METHODS: Nutrient inadequacy was determined in groups (CAN, n=12; USA, n=27) by the proportion of athletes with mean intakes below the estimated average requirement (EAR) using the Research Solutions Food Processor Diet Analysis Software (ESHA). RESULTS: Mean energy intakes for women and men were 1,603 +/- 855 kcal and 1906 +/- 756 kcal, respectively. Reported micronutrient intakes were below the EAR for >60 percent of USA athletes for vitamin D, folate, calcium, magnesium, potassium, and zinc, while 60 percent of CAN athletes reported intakes below the EAR in Niacin, B6, B12, vitamin C, vitamin D, folate, calcium, iron, magnesium, potassium, and zinc. CONCLUSION: Nutrient intakes below the EAR were consistently found for both groups of elite athletes with SCI. Further research is needed to examine nutrient intake using other methods of dietary assessment and to determine factors that may lead to nutrient insufficiencies among elite athletes with SCI.
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 | 0.001 | 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".