A Scoping Review of the Evidence for the Medicinal Use of Natural Honey in Animals
Bibliographic record
Abstract
Honey has a history of medicinal use that predates written records. In recent decades, there has been renewed interest in the use of honey in human medicine, particularly for the treatment of burns and other wounds. Several recent systematic reviews in the human literature have demonstrated the efficacy of honey in the treatment of a number of conditions, including burns, wounds and oral mucositis. The goal of this scoping review was to describe the nature and extent of the current body of evidence addressing the medicinal use of natural honey and/or its derivatives in animals. Although the focus of this review was the veterinary literature, all animal species except insects and humans were eligible, including animals used for biomedical research. Electronic databases searched were MEDLINE, CAB Abstracts, AGRICOLA, Web of Science Core Collection, and Web of Science SciELO Citation Index. A total of 397 articles reporting 436 primary research studies were included in this review. The majority of the articles were biomedical research articles (n = 350); fewer veterinary research articles were identified (n = 47). Apart from one systematic review, all biomedical studies were challenge trials. Most veterinary studies were case reports/series (n = 23), followed by challenge trials (n = 18) and controlled trials (n = 8). The animal species examined within veterinary articles consisted primarily of dogs, horses, cats and cattle, whereas the majority of biomedical research articles examined rats and mice. Wound healing was the most common indication examined; other indications examined included the prevention or treatment of gastric ulcers, bacterial and parasitic infections, toxic exposures, metabolic conditions (e.g., diabetes) and neoplasia. The majority of interventions consisted of non-medical grade honey (n = 412/436), followed by medical-grade honey (n = 29/436) and derivatives of natural honey (n = 9/436). With much of the current veterinary literature consisting of case reports and case series, high-quality primary veterinary research in the form of controlled trials or challenge trials is needed to advance this field, as well as to provide sound data for evidence-based assessments of the efficacy of honey in clinical veterinary practise.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| 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".