The effectiveness of sitting position during second stage of labor among primiparae on maternal and neonatal outcomes
Bibliographic record
Abstract
Background: There is longstanding debate concerning the most advantageous labor positions. Lithotomy position is the most common position used in tertiary settings, but the sitting position has been recommended more recently. Labor position in the second stage of labor affects maternal and neonatal outcomes. Therefore, the current study aims to compare the effectiveness of lithotomy and sitting positions during the second stage of labor on maternal and neonatal outcomes using a quasi-experimental design with purposeful sampling.Methods: Sample size: 120 low-risk primiparae, divided equally in sitting and lithotomy positions. Setting: Labor and delivery unit at King Abdulaziz University Hospital (KAUH), Jeddah. Sampling: Data collected over six months, from January to June 2020. Tool: A structured, five-part questionnaire. Data analysis: Chi-square test with post hoc Bonferroni test to examine significant differences between the two groups, using SPSS version 24.0.Results: Significant positive effects of sitting position are observed in reduced episiotomy rate and newborn transfer to the intensive care unit, shortened second stage of labor, improved mode of delivery, newborn arterial cord PH, Apgar score at one and five minutes of life, and maternal satisfaction (p-value < .05).Conclusions: The sitting position during the second stage of labor has more positive effects than the lithotomy position for maternal and neonatal outcomes. Recommendation: Women should have the right to be educated about the benefits of the sitting position during the second stage of labor.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".