Effect of Localized Cold Treatment Modality on Pain Intensity during Labor in Primiparous Women
Bibliographic record
Abstract
Introduction: Pain is a common and inevitable component of labor process and is considered as the most severe pain in human. Regarding the importance of reducing labor pain and prioritizing natural vaginal delivery, this study was conducted to determine the effect of localized cold treatment modality on labor pain in primiparous women. Methods: This clinical trial was conducted among 80 primiparous eligible women eligible in Karaj Maternity Hospitals, Karaj, Iran. The samples were randomly assigned into two groups of control and intervention. To determine the severity of pain and duration of delivery, McGill's Pain Questionnaire was used. Data analysis was performed using independent samples and paired t-tests in SPSS software, version 19. P-value less than 0.05 was considered statistically significant. Results: There was a significant difference between the groups regarding the severity of pain, mean duration of first and second stages of labor, frequency distribution of satisfaction of the subjects, satisfaction with breastfeeding and cuddling (P<0.05). However, no significant difference was observed between the groups in terms of the mean duration of the third stage of labor, mean Apgar scores, time of cuddling, and satisfaction with breastfeeding (P=0.49, P>0.05, P=0.43, and P=0.86, respectively). Conclusion: Cold treatment modality was useful for controlling labor pain in comparison to routine care.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".