Carbohydrate Considerations for Athletes with a Spinal Cord Injury
Bibliographic record
Abstract
The Paralympic movement is growing in popularity, resulting in increased numbers of athletes with a spinal cord injury (SCI) competing in various sport disciplines. Athletes with an SCI require specialized recommendations to promote health and to maximize performance, as evidenced by their metabolic and physiological adaptations. Nutrition is a key factor for optimal performance; however, scientifically supported nutritional recommendations are limited. This review summarizes the current knowledge regarding the importance of carbohydrates (CHO) for health and performance in athletes with an SCI. Factors possibly affecting CHO needs, such as muscle atrophy, reduced energy expenditure, and secondary complications are analyzed comprehensively. Furthermore, a model calculation for CHO requirements during an endurance event is provided. Along with assessing the effectiveness of CHO supplementation in the athletic population with SCI, the evaluation of their CHO intake from the available research supplies background to current practices. Finally, future directions are identified. In conclusion, the direct transfer of CHO guidelines from able-bodied (AB) athletes to athletes with an SCI does not seem to be reasonable. Based on the critical role of CHOs in exercise performance, establishing recommendations for athletes with an SCI should be the overall objective for prospective research.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".