Thyroid metabolism and supplementation: A review framed in sports environment
Bibliographic record
Abstract
OBJECTIVES: This paper aimed to consider those features that may suggest a link between thyroid hormones pharmacology and athletes' health based on current consumption trends in a population of athletes. METHODS: Methods used were observation, description, and synthesis, mainly. Among the documents reviewed were books, scientific articles, and review articles peer-reviewed. The review covered sources published in the period 1961 to 2021. Only references with a traceable origin were accepted (DOI numbering, ISSN, and ISBN, as well as peer-reviewed journals). The data on the consumption of thyroid hormones derivatives were extracted from the Doping Control Forms of athlete samples received at Laboratorio Antidoping FMSI of Rome from 2017 to 2021. RESULTS: An overview of the biosynthesis, pharmacology, and metabolism of thyroid hormones, including thyronamines and thyronacetic acids, was presented. Likewise, a summary is presented on the relationship between thyroid hormones and ethnic and gender differences, their physiology in sport, and the reasons why their use could be considered attractive for athletes. CONCLUSION: Today, thyroid hormones are not listed as a prohibited substance by the World Anti-Doping Agency. However, several requests to include levothyroxine on the prohibited list are documented. The observation that the number of athletes taking thyroid hormones is growing, particularly in sports such as cycling, triathlons, and skating, should prompt an update on this topic.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".