Development and evaluation of an ovarian hormone profile classification tool for female athletes: step one of a two-step process to determine ovarian hormone profiles
Bibliographic record
Abstract
Objective: This study aimed to develop a reliable, comprehensive and fit-for-purpose tool for classifying ovarian hormone profiles (OHPs) (step one of a two-step process) in postmenarcheal to perimenopausal female athletes. Methods: The OHP classification tool was designed by a team of sport scientists, practitioners and medics and is intended for use by sport practitioners. It incorporates self-reported data and guides subsequent verification methods. Written feedback was received from practitioners currently working with elite female athletes (n=5), ensuring its applicability in an applied sport setting. In addition, inter-user (n=2) and intra-user (n=30) repeatability was assessed. Results: All practitioners agreed that the online tool was user-friendly. Four (out of five) practitioners stated they would include the tool in their practice, with the fifth stating that they did not have the capacity to incorporate it in their practice at present. The OHP classification tool showed excellent test-retest reliability with Cronbach's alpha values exceeding 0.9. Conclusion: This tool facilitates the classification of OHPs and promotes discussions between athletes and practitioners, enhancing understanding and management of ovarian hormone health in sportswomen.
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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 | 0.033 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".