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Record W2045437937 · doi:10.1097/jpn.0000000000000023

Labor Down or Bear Down

2014· review· en· W2045437937 on OpenAlexaboutno aff
Kathryn Osborne, Lisa Hanson

Bibliographic record

VenueThe Journal of Perinatal & Neonatal Nursing · 2014
Typereview
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineStage (stratigraphy)ChildbirthCervixNursingObstetricsPregnancy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.958
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.049
GPT teacher head0.407
Teacher spread0.358 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2014
Admission routes1
Has abstractyes

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