Labor Down or Bear Down: A Strategy to Translate Second-Stage Labor Evidence to Perinatal Practice
Bibliographic record
Abstract
Scientific evidence supports spontaneous physiologic approaches to second-stage labor care; however, most women in US hospitals continue to receive direction from nurses and birth attendants to use prolonged Valsalva bearing-down efforts as soon as the cervix is completely dilated. Delaying maternal bearing-down efforts during second-stage labor until a woman feels an urge to push (laboring down) results in optimal use of maternal energy, has no detrimental maternal effects, and results in improved fetal oxygenation. Although most commonly used with women who are undergoing epidural anesthesia, laboring down is just one component of physiologic second-stage labor care that can be used to achieve optimal maternal and neonatal outcomes for women with or without an epidural. Prior efforts to translate evidence regarding second-stage labor care to practice have not been successful. In this article, the scientific evidence for second-stage labor care and previous efforts at clinical translation are reviewed. The Ottawa Hospital Second Stage Protocol is presented as a model with potential to allow translation of evidence to practice. Recommendations to enhance widespread adoption of evidence-based practice are provided, including improved collaboration between nurses and birth attendants.
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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 | 0.363 | 0.486 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".