Performance of a Modified, Three-Step Menstrual Cycle Tracking Method in Naturally Cycling Females
Bibliographic record
Abstract
Background: An objective method of menstrual cycle tracking while minimizing participant burden and cost for field-based research is needed. A modification was proposed to a well-known three-step (m3-step) method to improve accessibility for participants and athletes with difficult travel schedules. Methods: A longitudinal design was employed, and the m3-step method (calendar counting, urinary ovulation, and salivary hormones) was completed over three consecutive cycles to assess performance while classifying menstrual variability. Naturally cycling females (N=28; age 18-36 years) from across Canada were recruited prospectively. Participants shipped their hormone samples to the lab where they were classified as “high” or “low” hormone based on ovulation status and a progesterone/estradiol (P4/E2) ratio of 100 pg/mL. Cycle length (mean, ±; SD) was self-reported (28.9 ± 4.16 days) and salivary testing occurred on cycle day 22.5 ± 3.26. Results: The average luteinizing hormone surge for those with a positive test occurred on cycle day 14.2 ± 2.27 (22/28). Average cycle length (t (24.1) = 2.44, p = 0.02), progesterone (t (21.1) = -4.72, p 0.01) and P4/E2 ratios (t (18.9) = -7.74, p 0.01) were statistically significant between high (12/28) and low (16/28) hormone groups. A logistic regression explored the relationship of progesterone to the hormone classification criteria using a crudes odd ratio (1.98 (95% CI 1.24 – 3.17, p 0.01)). Conclusion: The m3-step method yielded a sensitivity of 65% and specificity of 91% using the P4/E2 ratio of 100 pg/mL. Limitations included self-reported naturally cycling, the day of the testing and the P4/E2 value used. In summary, this study examined the feasibility of a m3-step menstrual cycle tracking method to classify hormones as high or low in naturally cycling females for potential implementation in a field-based setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".