The Labour Pain and Psychosocial Factors for the Choice of epidural Analgesia
Bibliographic record
Abstract
Aims: To describe the type of pain and Labour to Evaluate Factors for epidural analgesia (EA) choice. Methods: 98 parturients were recruited in this prospective observational study. The Labour pain intensity was measured on a visual analog scale before the first vaginal exam and every time as vaginal exam was Performed; parturients completed pain catastrophizing (PC) and the McGill questionnaire and were asked about plans to choose Labour Analgesia. Demographic Data and questions about overall childbirth experience were measured on first day after vaginal delivery. Results: The cohort comprised 34 (34.7%) women with EA. 36 (36.7%) women Reported plans to use EA at the time They had arrived the delivery-room and the EA was done for 27 (75%) of women under these (p <0.05). The first vaginal exam had Revealed That women with preconception and enough information about EA had arrived the delivery room with less cervical dilatation (p = 0:01). PC and pain intensity During first vaginal exam did not influencing the choice of EA (p> 0:05). Uterini contractions pain intensity correlated with the choice of EA (p = 0.027). Labour prodromal pain described as pulling, dreadful. Active stage pain is Troublesome and annoying, pressing and pulsing. Labour analgesia has not impact on the pain description (p <0.05). Conclusion: PC and pain of vaginal exam have no Influence on EA's choice, but women mood and expectation are important factor. Emotional and sensual components are equaled important to describe the Labour pain. During the process of the Labour choice of affective descriptors Differ from reflecting the anxiety Describing tiredness, while sensor components do not change, only intensity of pain varies.
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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 | 0.000 |
| 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) | 0.005 | 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".