Bibliographic record
Abstract
The Association of Women's Health Obstetrical and Neonatal Nurses (AWHONN) has launched a Peanut Ball campaign to help curb the high rate of cesarean births in the United States. Cesarean births are especially likely in women who receive epidural anesthesia due to immobility and pelvic laxity. The peanut ball (PB) is a birthing ball that when placed between the mother's legs can increase pelvic dimensions and facilitate fetal descent and birth. For PB to increase vaginal deliveries (VDs), nurses on obstetrical wards need to “buy in” to using this innovation. Having “innovator” nurses on the shift helped disseminate the PB intervention and increased the rate of VDs. Using a retrospective study design that uses data collected for non-research purposes saves time and cost. Our retrospective study examined the difference in VDs with patient controlled epidural anesthesia (PCEA) in the first five months of 2016 prior to PB use compared with the same months in 2017 post intervention. Using a paired t-test we found a significant difference of successful PCEA vaginal births in 2016 compared to 2017 (p = .008). This relatively inexpensive and easy survey can be done by most obstetrical services and help AWHONN in their campaign to decrease the rate of cesarean sections.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".