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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".