Outcomes of a honey and olive oil‐based dressing material on wounds of women that have had a caesarean section in south‐western Uganda
Bibliographic record
Abstract
Postpartum sepsis following caesarean sections among women in Uganda remains a risk. However, erratic supplies of standard dressing material make the risk a reality. Alternative wound dressing materials, therefore, remain a viable option. This study examined the outcome of a honey and olive oil-based dressing material on caesarean section wound healing. Using the Uganda Industrial Research Institute (UIRI), the study team created I-Dress, a wound dressing made of honey and olive oil. A case-series study was conducted on 25 women who received I-Dress following caesarean births at three health facilities in south-west Uganda. Vital signs (temperature, blood pressure, and heart rate) were measured, along with wound induration, dehiscence, and discharge. The primary outcome was the length of time it took for the wound to heal. The average age of the women who received I-Dress was 27.5 (±4.8) years. Following the application of I-Dress, all of the women (100%) had no wound induration or dehiscence, with only two (13%) experiencing wound discharge. The vast majority of the women (88%) had never had a Caesarean section before. The mean time required for wound healing was 2.2 (±0.7) days. The findings indicate that honey and olive oil-based dressings improve Caesarean section wound healing in terms of wound induration, dehiscence, and time to healing. These dressing materials can therefore supplement the standard dressing materials, especially in resource-constrained settings.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".