© 2010 Canadian Medical Association or its licensors
Bibliographic record
Abstract
in endurance sports have been shroudedin controversy for the past 30 years because manyathletes, intent on winning races and achieving faster times, have turned to banned performance-enhancing drugs. The World Anti-Doping Agency’s 2009 list of prohibited substances and methods includes erythropoietin, other erythropoiesis-stimulating agents and various methods designed to enhance oxygen transfer.1 The effects of these agents and methods are thought to be primarily beneficial in endurance sports such as distance running and cycling. Despite the prohibition, the use of these drugs and tech-niques persists, as evidenced by continued positive results of drug tests in and out of competition. Altitude training has also become common among endurance athletes, because it has been associated with an increase in performance and in serum hemoglobin and hema-tocrit levels. However, this response is transient — the phys-iologic variables return to their baseline soon after the athlete returns to sea level.2 This moderate performance benefit is outweighed by several severe and life-threatening risks, including pulmonary edema,3 cerebral edema4 and severe flatulence.5 While athletes endanger their careers and well-being in attempts to gain small benefits with illicit or inconvenient practices, a legal, nonprescription alternative has been largely ignored by athletes, coaches and exercise physiologists alike. Cigarette smoking has been shown to increase serum hemo-globin and hematocrit levels, increase lung volume and stimu-late weight loss — characteristics all known to enhance per-formance in endurance sports. This paper will discuss the potential benefits of cigarette smoking to endurance perfor-mance and make recommendations as to how individuals and national bodies could effectively integrate this practice into high-performance training programs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.857 | 0.801 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".