Effect of hydro-alcoholic extract of clove on intensity of episiotomy pain in women: randomized clinical trial
Bibliographic record
Abstract
Introduction: One of the surgical interventions in the process of natural vaginal delivery is episiotomy. Episiotomy pain is a stressful problem for mothers at postpartum. Nowadays, more attention has been paid to the use of pain reduction techniques in traditional medicine such as clove extract. Therefore, this study was performed with aim to evaluate the effect of hydroalcoholic extract of clove buds on the severity of episiotomy pain in normal vaginal delivery. Methods: This triple-blind clinical trial study was performed on 80 mothers who had delivery in the hospitals of Ferdows, Gonabad and Mashhad in 2019. The subjects were divided into two groups of intervention and placebo using randomized blockade. The intervention group received clove extract and the placebo group received distilled water. The two groups received the solutions twice a day for 10 days after delivery. Demographic and midwifery checklists and pain visual standardized tool and McGill tool were used for data collection. Pain severity was measured in several stages (before intervention, on day 4 and day 10 after delivery). Data were analyzed by SPSS software (version 22) and paired t and Chi-square tests. P < 0.05 was considered statistically significant. Results: There was no statistically significant difference in pain severity between the two groups before the intervention (p = 0.77). There was significant difference in pain severity between the two groups at day 4 (p = 0.00) and 10 (p = 0.00) after delivery. Conclusion: The use of clove extract reduces the severity of episiotomy ulcer pain after delivery. Therefore, it is recommended to use it for pain relief after episiotomy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".