Achieving Pregnancy Using Primary Care Interventions to Identify the Fertile Window
Bibliographic record
Abstract
OBJECTIVE: To determine the effectiveness of achieving pregnancy with focused intercourse in the fertile window identified using natural fertility indicators. METHODS: 24-cycle prospective effectiveness study. SETTING: A North American web-based fertility monitoring service. PARTICIPANTS: 256 North American women aged 20-43 (mean age 29.2 years) seeking to achieve pregnancy. INTERVENTION: Participants identified their fertile window with either electronic hormonal fertility monitoring or cervical mucus monitoring, or both, and recorded their observations on an online fertility tracking system. MAIN OUTCOME MEASURES: Pregnancies were validated by nurses with an online self-assessed pregnancy evaluation form. Survival analysis was used to determine pregnancy rates. RESULTS: There were 150 pregnancies among the 256 participants with an overall pregnancy rate of 78 per 100 women over 12 menstrual cycles. There were 54 pregnancies (68%) among the 80 women using the fertility monitor, 11 pregnancies (46%) among the 24 women using mucus monitoring, and 90 (63%) among the 143 women using both mucus and monitor. The 12-cycle pregnancy rates per 100 women were 83 (monitor group), 72 (mucus group), and 75 (mucus and monitor group). Pregnancy rates reached 100% at 24 cycles of use for those women using the hormonal fertility monitor. CONCLUSION: Use of the hormonal fertility monitor alone seems to offer the best natural estimate of the fertile phase of the menstrual cycle for women wishing to achieve a pregnancy. Focusing intercourse through 24 menstrual cycles can be beneficial for achieving pregnancy.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".