Bibliographic record
Abstract
目的 探讨陪伴待产配合自由体位管理在自然分娩初产妇中的应用效果。方法采用简单随机化分组方法，将2013年4-10月自然分娩的168例初产妇分为观察组和对照组，每组84例。当产妇宫口开至3 cm后，观察组产妇采用陪伴待产配合自由体位分娩助产，由家属共同参与产妇体位选择；对照组产妇采用传统卧位或半卧位姿势待产及分娩，分娩时无家属陪伴。两组产妇均有导乐护士一对一陪伴。观察两组产妇产程时间。两组产妇均于分娩后采用简化McGill疼痛问卷（ SF-MPQ），分娩控制量表（ LAS），视觉模拟量表（ VAS）分别评价产痛、分娩控制感、焦虑程度。结果观察组第一产程时间为（450．62±132．38）min，对照组为（524．61±128．94）min，观察组短于对照组，差异有统计学意义（t＝-2．689，P＜0．05）；两组第二产程、第三产程时间差异无统计学意义（t值分别为-1．521，-1．336；P＞0．05）。观察组VAS、VRS、PPI、VAS-A 得分分别为（6．07±1．46），（19．89±3．26），（2．78±0．83），（3．57±1．17）分，对照组分别为（8．39±1．58），（28．76±2．87），（3．92±0．88），（6．29±1．28）分，观察组均低于对照组，差异均有统计学意义（t值分别为-7．786，-9．987，-6．238，0．876；P＜0．05），LAS得分观察组为（171．47±18．35）分，对照组为（123．26±18．37）分，观察组高于对照组，差异有统计学意义（t＝0．472，P＜0．05）。结论陪伴待产配合自由体位分娩能够减少初产妇自然分娩时的产痛，缓解焦虑情绪，提高产妇的分娩控制感，有利于缩短第一产程，促进正常分娩。
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".