Why We Must Stop Assuming and Estimating Menstrual Cycle Phases in Laboratory and Field-Based Sport Related Research
Bibliographic record
Abstract
The increased growth, popularity, and media interest in women's sport has led to calls for greater prioritisation of female-specific research and innovation. In response, science and medicine researchers have increased the volume of sport-related studies investigating female-specific matters, such as the menstrual cycle. Whilst the accelerated rate of published studies with female participants is welcome, the emerging trend of using assumed or estimated menstrual cycle phases to characterise ovarian hormone profiles is a significant concern. Replacing direct measurements of key characteristics of the menstrual cycle (e.g. the surge in luteinising hormone prior to ovulation via urine detection and sufficient luteal phase progesterone via blood or saliva sampling) with assumptions or estimates (i.e. no measurements) is proposed to be a pragmatic and convenient way of generating data, particularly in field-based research (i.e. elite athlete environments), where time, resources, and athlete availability are sometimes constrained. Using assumed or estimated phases, however, amounts to guessing the occurrence and timing of ovarian hormone fluctuations and risks potentially significant implications for female athlete health, training, performance, injury, etc., as well as resource deployment. The positive intentions of researchers and scientific journals in this space are not in question. The aim of this Current Opinion is to explain why using assumed or estimated menstrual cycle phases is an approach that has little scientific basis and lacks the rigour and appropriate methodological quality to produce valid and reliable data. In doing so, we provide evidence-based responses to common speculation points and offer recommendations for future research.
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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 | 0.336 | 0.566 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".