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Record W4403310760 · doi:10.53555/sfs.v10i1.2338

Clinical Trial: Bee Honey As Topical Treatment For Infected Chronic Wound

2023· article· en· W4403310760 on OpenAlexvenueno aff
Mahasin Wadi, Talal Mohammed Geregandi, Hanan Alyami

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBee Products Chemical Analysis
Canadian institutionsnot available
FundersPrincess Nourah Bint Abdulrahman University
KeywordsHoney beeMedicineDermatologyBee venomChronic woundWound healingBiologySurgeryBotanyZoology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.354
GPT teacher head0.362
Teacher spread0.008 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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