Comparison of the Effects of Maternal Supportive Care and Acupressure (at BL32 Acupoint) on Labor Length and Infant’s Apgar Score
Bibliographic record
Abstract
BACKGROUND & OBJECTIVES: Prolonged labor leads to increase of cesarean deliveries, reduction of fetal heart rate, and maternal as well as infantile complications. Therefore, many women tend to use pharmacological or non-pharmacological methods for reduction of labor length. The present study aimed to compare the effects of maternal supportive care and acupressure (at BL32 acupoint) on labor length and infant's Apgar score. METHODS: In this clinical trial, 150 women with low-risk pregnancy were randomly divided into supportive care, acupressure, and control groups each containing 50 subjects. The data were collected using a questionnaire including demographic and pregnancy characteristics. Then, the data were analyzed using Chi-square test and one-way ANOVA. RESULTS: The mean length of the first and second stages of labor was respectively 157.0±29.5 and 58.9±5.8 minutes in the supportive care group, 161.7±37.3 and 56.1±31.4 minutes in the acupressure group, ad 281.0±9.8 and 128.4±44.9 minutes in the control group. The difference between the length of labor stages was significant in the three study groups (P<0.001). Moreover, the frequency of Apgar score>8 in the first and 5th minutes was higher in the supportive care and acupressure groups compared to the control group, and the difference was statistically significant (P<0.001). CONCLUSION: Continuous support and acupressure could reduce the length of labor stages and increase the infants' Apgar scores. Therefore, these methods, as effective non-pharmacological strategies, can be introduced to the medical staff to improve the delivery outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".