Use of Ovulation Predictor Kits as Adjuncts When Using Fertility Awareness Methods (FAMs): A Pilot Study
Bibliographic record
Abstract
PURPOSE: Difficult clinical signs such as confusing cervical mucus or erratic basal body temperature can make the use of fertility awareness methods (FAMs) difficult in some cases. The goal of this study was to assess the feasibility of using a cheap urinary luteinizing hormone (LH)-surge identification kit as an adjunct to identify the infertile phase after ovulation when facing these scenarios. METHODS: The study used a block-allocation, crossover, 2-arm methodology (LH kit/FAM vs FAM only). Comparison of the 2 arms was done with regard to the accuracy of identification (yes/no) of the luteal phase in each cycle as confirmed by serum progesterone concentrations. RESULTS: We recruited 23 Canadian women currently using FAM, aged 18 to 48 years, who have had menstrual cycles 25 to 35 days long for the past 3 months and perceive themselves to have difficulty with identifying the infertile phase after ovulation. LH kits identified 100% of the luteal phases, whereas FAM indentified 87% (statistically significant). In those identified cycles, LH kits provided a mean of 10.3 days of infertility, and FAM only provided 10 days of infertility (not statistically significant). CONCLUSIONS: Among this population, LH kits may offer an adjunct for women who may wish to have an additional double-check. However, there are still clinical circumstances when even an LH kit does not provide confirmation. More research in this area is encouraged.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".