Contraceptive Outcomes of the Natural Cycles Birth Control App: A Study of Canadian Women
Bibliographic record
Abstract
Abstract Objective This study aimed to investigate the key demographics and evaluate the real-world contraceptive failure and continuation rates of the Natural Cycles app in a cohort of women from Canada. Methods This was a real-world, prospective cohort study. Demographics were assessed via in-app questionnaires. Contraceptive failure rates in typical and perfect use were calculated using the 13-cycle cumulative pregnancy probability (Kaplan-Meier survival analysis) and the one-year Pearl Index (PI). One-year continuation rates were estimated through survival analysis. Results The study included 8 ‘798 women who contributed an average of 9.2 months of data, amounting to a total of 7’ 063 woman-years of exposure. The average user was 27.3 years old, had a body mass index of 24.6, and reported being in a stable relationship. With typical use, the app demonstrated a 13-cycle cumulative pregnancy probability of 4.8 [95% CI: 4.3, 5.4] and a Pearl Index of 4.3 [95% CI: 3.9, 4.8]. Under perfect use, the contraceptive failure rate was 2.3 [95% CI: 0.7, 3.9] for life table analysis and 1.7 [95% CI: 0.5, 2.8] for the 1-year PI. The contraceptive method’s continuation rate after one year was 62.4%. Conclusions The data presented in this study offer valuable insights into the cohort of women using the Natural Cycles app in Canada and provide country-specific effectiveness estimates. The app’s contraceptive effectiveness aligns with previously published data on Natural Cycles.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".