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Record W4385792581 · doi:10.1111/add.16315

Cannabis and sport: A World Anti‐Doping perspective

2023· editorial· en· W4385792581 on OpenAlexaff
Thomas J. Hudzik, Marilyn A. Huestis, Sabina Strano Rossi, Yorck‐Olaf Schumacher, Peter Harcourt, Richard Budgett, Mark Stuart, J.N.A. Tettey, Irene Mazzoni, Olivier Rabin, Anne Danion, Michael D. Culler, David J. Handelsman, Mario Thevis, Audrey Kinahan

Bibliographic record

VenueAddiction · 2023
Typeeditorial
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsWorld Anti-Doping Agency
Fundersnot available
KeywordsCannabisAthletesCannabidiolMedicinePopulationPsychiatryPsychologyFamily medicineEnvironmental healthPhysical therapy

Abstract

fetched live from OpenAlex

Respect for self and other participants: the welfare and safety of other participants may be compromised by impaired judgment associated with the presence of cannabis in an athlete incompetition.Based upon all the above considerations, which included input from the Ethics and Athletic committees at WADA, it was concluded that cannabis use met the Spirit of Sport criterion.WADA emphasizes that prohibition of cannabis is in-competition only, which is defined as after 23:59 hours on the day prior to competition.The current decision limit of 180 ng/ml of Δ9-THC-COOH in urine and a cut-off of 150 ng/ml, plus the uncertainty of measurement of 30 ng/ml, takes this into account.Because of these high thresholds, primarily chronic, frequent cannabis users and athletes consuming high doses in-competition will be detected.Therefore, the cut-off generally will not affect the freedom of an athlete who wishes to legally consume cannabis outside of competition.Athletes who have a need for medicinal cannabis treatment should request a therapeutic use exemption (TUE).

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)0.003
metaresearch head score (Gemma)0.005
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.005
Scholarly communication0.0070.007
Open science0.0020.002
Research integrity0.0100.010
Insufficient payload (model declined to judge)0.0080.002

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 head0.011
GPT teacher head0.309
Teacher spread0.298 · 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
GenreEditorial

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

Citations8
Published2023
Admission routes1
Has abstractyes

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