Level of Agreement Between a Modified, Three-Step Menstrual Cycle Tracking Method and a Female-Health Menstrual Cycle Tracking App
Bibliographic record
Abstract
Background: A need exists to incorporate evidence-based tracking methods that measure menstrual cycle (MC) variability to describe the data quality provided by an app. The study purpose was to assess the agreement between an app’s cycle phase identifications and a modified version of the three-step method (m3stepMC) of hormone verification. Materials and Methods: Participants across Canada were recruited to track their MC over 3 months by entering data into a female-health MC tracking app (the app) while collecting measures of ovulation and salivary hormones around the late-follicular (FP) and mid-luteal (MLP) phases, respectively. Bland–Altman plots assessed the limits of agreement (LoA) between the identified days within each of the app’s predetermined phases and the m3stepMC-identified days when MC dates aligned between the methods. Pearson’s correlations ( r ) were used to examine the effect size of relationships between variables. Results: Participants’ ( n = 25) mean age was 29.3 ± 4.24 with self-reported mean cycle lengths of 27.3 ± 2.38 days. The agreement between the app’s estimated (1) end of phase one and the estimated start of the mid-FP was 0.6 ± 1.66 days (95% LoA: 2.65–3.85; r = 0.66), (2) end of phase two and the identified luteinizing hormone (LH) surge day and midpoint of phase three and the estimated 48-hour ovulatory window post-LH surge day were −0.6 ± 1.71 days (95% LoA: −3.95 to 2.75; r = 0.64), and (3) phase four and the estimated MLP day verified by salivary hormones and the start of the app’s phase five and the estimated late-luteal midpoint day were −2.2 ± 0.97 (95% LoA: −4.13 to −0.32; r = 0.94). Conclusion: This study describes the agreement between a m3stepMC tracking method and hormone measures and an app’s predetermined MC phase system in eumenorrheic cycles when MC dates aligned between methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.080 | 0.175 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".