Bibliographic record
Abstract
The use of performance-enhancing drugs is an unfortunatereality of contemporary sport. It would be a mistake tobelieve that this is a phenomenon found only in elite sport.Athletes at all levels and young adults may be tempted toaccentuate performance or physique with prohibited drugsor products marketed as supplements. No defined populationsof users ingesting known quantities of known substances are generally available for study. Many of these products have been associated with adverse health effects; cardiac structure and function are known to be affected by many of the products commonly abused. Changes to the lipoprotein profile, propensity for coagulation, coronary circulation, and ventricular function may accompany the use of many performance-enhancing compounds and methods. Anabolic steroids, other peptide hormones, stimulants, erythropoietin and blood doping, have all been associated with significant cardiovascular consequences. So-called nutritional supplements aggressively marketed to the athletically inclined, are available over the Internet and typically totally unregulated in the country of their origin. Clinicians should be aware of the problems that such drug use can engender, and be sensitive to the possibilities of such abuse in caring for athletes and young patients, particularly in those presenting with unusual or unanticipated cardiovascular signs and symptoms.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".