Helping Patients to Predict and Confirm Ovulation with the Use of Combined Urinary Hormonal and Smartphone Technology: A Proof-of-Concept Retrospective Descriptive Case Series
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Smartphone-based fertility awareness methods with home-based urinary hormonal testing are gaining popularity for fertility tracking. In our university-affiliated family practice, we integrated a previously developed ovulation tracking application into a protocol for monitoring urinary sex hormones and cervical secretions. Serum progesterone was used to confirm the luteal phase, with levels ≥ 15.9 nmol/L ensuring confirmation. Data from 110 women seen for infertility treatment (n = 95) or family planning advice (n = 15) and using our ovulation prediction protocol showed that most opted for a combination of cervical mucus and luteinizing hormone testing (n = 86). Among those using it for family planning, the median usage among women spanned 56 cycles, and 13 cycles per woman required progesterone testing for confirmation. Thirteen patients are still using the method without unintended pregnancies. No unintended pregnancies occurred. Confidence in tests based on serum progesterone was high (93%). For infertility, the method helped in the identification of anovulation, evaluating treatment response, and in diagnosing subfertility causes. This proof-of-concept retrospective descriptive case series suggests the potential for smartphone-based monitoring in fertility management, urging further studies for application enhancements and prospective validation.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it