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Informed Decision-Making on Sympathomimetic Use in Sport and Health

2002· review· en· W2321235781 on OpenAlexaff
Ron A. Bouchard, Anna R. Weber, Jonathan D. Geiger

Bibliographic record

VenueClinical Journal of Sport Medicine · 2002
Typereview
Languageen
FieldMedicine
TopicPharmacology and Obesity Treatment
Canadian institutionsUniversity of ManitobaSt. Boniface Hospital
FundersNational Institute of Neurological Disorders and StrokeNational Heart, Lung, and Blood InstituteNational Institute on Aging
KeywordsMedicineAthletesAgency (philosophy)Elite athletesMedical prescriptionAction (physics)EliteHealth benefitsPharmacologyPhysical therapyTraditional medicineLaw

Abstract

fetched live from OpenAlex

The International Olympic Committee, the World Anti-Doping Agency, and International Sport Federations have banned and restricted the use of many stimulants including prescription and over-the-counter medications and dietary supplements. In addition to elite athletes, people of all ages use stimulants in attempts to improve athletic performance, alter body composition, and increase levels of energy. Here we introduce a seven-stage model designed to facilitate informed decision-making by individuals taking or thinking of taking stimulants for sport, health, and/or appearance reasons. We review for amphetamines, over-the counter sympathomimetics, and caffeine their performance-enhancing and performance-degrading effects, health benefits and mechanisms of action, medical side effects, and legal, ethical, safety, and financial implications.

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.222
GPT teacher head0.532
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations40
Published2002
Admission routes1
Has abstractyes

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