Aloe Vera Gel and Cesarean Wound Healing; A Randomized Controlled Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Failure in complete healing of the wound is one of the probable complications of cesarean. The present study aimed to determine the effectiveness of dressing with aloe vera gel in healing of cesarean wound. METHODS: This prospective randomized double-blind clinical trial was conducted on 90 women who had undergone cesarean operation in Amir-al-Momenin hospital (Gerash, Iran). The participants were randomly divided into two groups each containing 45 patients. In one group, the wound was dressed with aloe vera gel, while simple dressing was used in the control group. Wound healing was assessed 24 hours and 8 days after the cesarean operation using REEDA scale. The data were analyzed through Chi-square and t-test. RESULTS: The participants' mean age was 27.56±4.20 in the aloe vera group and 26.62±4.88 in the control group, but the difference was not statistically significant. However, a significant difference was found between the two groups concerning body mass index, heart rate, and systolic blood pressure (P<0.05). Also, a significant difference was observed between the two groups with respect to the wound healing score 24 hours after the operation (P=0.003). After 8 days, however, the difference in the wound healing score was not significant (P=0.283). Overall, 45 participants in the aloe vera group and 35 ones in the control group had obtained a zero score 24 hours after the operation. These measures were respectively obtained as 42 and 41eight days after the operation. CONCLUSION: According to the findings of this study, the women are recommended to be informed regarding the positive effects of dressing with aloe vera gel.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 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) | 0.006 | 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".