Bibliographic record
Abstract
Pharmacists often overlook athletes as patients who might benefit from trusted pharmaceutical advice.Many athletes are strongly motivated to seek techniques or substances to optimize performance.Any strategy to increase performance, from anabolic steroids to visualization techniques, can be referred to as an ergogenic aid. 1,2Most pertinent to pharmacists are the regulated pharmaceutical ergogenic aids, such as natural health products (NHPs) or supplements. 1A recent survey of college athletes indicated that 42% had used supplements within the previous year. 2With high use, pharmaceutical support is required.Ample misinformation surrounds NHPs and supplements.Product marketing overemphasizes the benefits and minimizes the shortcomings.However, athletes continue to financially fuel the industry, hoping that marketing claims are factual.When selecting supplements, athletes often place high value on testimonial evidence.As pharmacists-grounded in evidence-an opportunity exists to improve care.As Peter J. Ambrose, Doping Control Officer of the Sydney and Beijing Olympics, states, "Pharmacists are primed to recommend products supported by efficacy and safety data while considering an athlete's specific needs." 3 Accompanying this opportunity is notable demand from athletes.In the United States, one study showed that over 1000 drug or NHP inquiries were made to the National Collegiate Athletic Association each month between July 2009 and June 2010.
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 | 0.007 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.006 |
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".