Dietary Supplementation for Para-Athletes: A Systematic Review
Bibliographic record
Abstract
The use of dietary supplements is high among athletes and non-athletes alike, as well as able-bodied individuals and those with impairments. However, evidence is lacking in the use of dietary supplements for sport performance in a para-athlete population (e.g., those training for the Paralympics or similar competition). Our objective was to examine the literature regarding evidence for various sport supplements in a para-athlete population. A comprehensive literature search was conducted using PubMed, SPORTDiscus, MedLine, and Rehabilitation and Sports Medicine Source. Fifteen studies met our inclusion criteria and were included in our review. Seven varieties of supplements were investigated in the studies reviewed, including caffeine, creatine, buffering agents, fish oil, leucine, and vitamin D. The evidence for each of these supplements remains inconclusive, with varying results between studies. Limitations of research in this area include the heterogeneity of the subjects within the population regarding functionality and impairment. Very few studies included individuals with impairments other than spinal cord injury. Overall, more research is needed to strengthen the evidence for or against supplement use in para-athletes. Future research is also recommended on performance in para-athlete populations with classifiable impairments other than spinal cord injuries.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".