Making Decisions About Supplement Use
Bibliographic record
Abstract
The use of dietary supplements is widespread among athletes in all sports and at all levels of competition, as it is in the general population. For the athlete training at the limits of what is sustainable, or for those seeking a shortcut to achieving their aims, supplements offer the prospect of bridging the gap between success and failure. Surveys show, however, that this is often not an informed choice and that the knowledge level among consumers is often low and that they are often influenced in their decisions by individuals with an equally inadequate understanding of the issues at stake. Supplement use may do more harm than good, unless it is based on a sound analysis of the evidence. Where a deficiency of an essential nutrient has been established by appropriate investigations, supplementation can provide a rapid and effective correction of the problem. Supplements can also provide a convenient and time-efficient solution to achieving the necessary intake of key nutrients such as protein and carbohydrate. Athletes contemplating the use of supplements should consider the potential for both positive and negative outcomes. Some ergogenic supplements may be of benefit to some athletes in some specific contexts, but many are less effective than is claimed. Some may be harmful to health of performance and some may contain agents prohibited by anti-doping regulations. Athletes should make informed choices that maximize the benefits while minimizing the risks.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".