Effectiveness of a Postpartum Breastfeeding Protocol for Avoiding Pregnancy
Bibliographic record
Abstract
The uses of cervical mucus and basal body temperature as indicators of return to fertility postpartum have resulted in high unintended pregnancy rates. In 2013, a study found that when women used urine hormone signs in a postpartum/breastfeeding protocol this resulted in fewer pregnancies. To improve the original protocol's effectiveness, three revisions were made: (1) women were to increase the number of days tested with the Clearblue Fertility Monitor, (2) an optional second luteinizing hormone test could be done in the evening, and (3) instructions were given to manage the beginning of the fertile window for the first six cycles postpartum. The purpose of this study was to determine the correct and typical use effectiveness rates to avoid pregnancy in women who used a revised postpartum/breastfeeding protocol. A cohort review of an established data set from 207 postpartum breastfeeding women who used the protocol to avoid pregnancy was completed using Kaplan-Meier survival analysis. Total pregnancy rates that included correct and incorrect use pregnancies were eighteen per one hundred women over twelve cycles of use. For the pregnancies that met a priori criteria, the correct use pregnancy rates were two per one hundred over twelve months and twelve cycles of use and typical use rates were four per one hundred women at twelve cycles of use. The protocol had fewer unplanned pregnancies than the original, however, the cost of the method increased.
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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 | 0.030 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".