Correction of misinterpretations and misrepresentations of the female athlete triad
Bibliographic record
Abstract
papers that followed on the same topic, and therefore we comment only on what we read.Language is extremely important in communicating scientific findings to peers and, more importantly, to the public.Thus, it is important to be as precise as possible.The public, in turn, must weigh a given risk against other risks they are willing to assume on a daily basis (eg, driving a car, smoking cigarettes, eating French fries or not exercising), and this ''risk mix'' ultimately will influence individual risk perception and behaviour.It is our opinion that in many instances concerning the female athlete triad, the data do not match the sensational language often used to warn young girls and women of ''the risks associated with exercise''.Further, although the social marketing value of the catchphrase ''female athlete triad'' is high, it connotes something bigger than what can actually be measured properly, and, frankly, is insulting to most women athletes who train and compete hard, bear children, and continue towards a healthy and successful older age.Indeed, if undernutrition (ie, low energy availability) in sports is the primary issue at hand, then any position stand and subsequent papers to this effect should be titled as such, and should be directed towards health consequences for male and female athletes.Finally, a position stand from the American College of Sports Medicine or any other organisation attempting to influence practice and policy should be evidence based and should rely on the highest quality data and not primarily on those generated from consensus or from the same group of researchers.We remain grateful for the opportunities to state our opposing views on the female athlete triad.Such opportunities have allowed us to confront several difficult issues that are sociopolitical as well as scientific.As scientists, we should (with respect) agree to disagree on the specific areas of contention concerning the triad and trust that individuals will make informed choices about their own behaviour based on the best available knowledge.
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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 | 0.052 | 0.459 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.017 | 0.027 |
| Insufficient payload (model declined to judge) | 0.019 | 0.019 |
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".