Clinical Trial: Bee Honey As Topical Treatment For Infected Chronic Wound
Bibliographic record
Abstract
Background: Many studies have demonstrated that honey has antibacterial activity in- vitro. Honey was proved clinically to be effective to severe infected chronic wounds not responding to conventional dressing. Objective: To evaluate the effect of topical application of honey to chronic wound with infected necrotic tissue. Methods: A female 48 year old was admitted to Khartoum North Teaching Hospital, with open fracture of tibia and fibula with chronic infected ulcerated wound. Swabs were taken from the infected wound for isolation and identification and viable bacterial count of the causative organism. Primary care of the wound was done which required grafting with split-thickness skin grafts and dressing with MEBO ointment was used. Daily topical application of honey was used instead of MEBO ointment. Results: Isolated organism has been identified as Staphylococcus aureus, an In vitro antibacterial test of honey showed significant activity against the isolated organism 28mm inhibition zone. Daily topical application of honey resulted in clean healthy granulation tissue after 2 weeks of treatment. Wound was reduced in size with healthy granulation tissue and split skin graft was done and complete healing was obtained after 6 weeks. Conclusion: Honey accelerates wound healing with promotion of healthy granulation tissue in short period. Honey exerted strong antibacterial activity against infected organism and helped graft taking.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".