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Record W3041238301 · doi:10.1136/vr.m2538

Does manuka honey improve the speed of wound healingin dogs?

2020· review· en· W3041238301 on OpenAlexaboutno aff
Marnie Brennan, Zoe Belshaw

Bibliographic record

VenueVeterinary Record · 2020
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicBee Products Chemical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsManuka HoneyMedicineSurgeryDog biteCanisAbrasion (mechanical)Wound healingPathologyBiology

Abstract

fetched live from OpenAlex

Henry, a two-year-old Labrador retriever, has been involved in a road traffic accident in which he partially degloved his distal right forelimb. You think you can repair some of the damage with surgery, but know that some areas will have to heal by secondary intention. You've recently been on a continuing professional development course about wounds that recommended applying manuka honey to aid wound healing. You wonder whether adding medical-grade manuka honey to your usual dressing protocol would improve the rate of healing. In [dogs with acute superficial wounds] does using [a dressing plus medical-grade manuka honey versus a dressing alone] result in [more rapid wound healing]? The search terms (dog.mp. OR dogs.mp. OR canine.mp. OR canines.mp. OR Canis.mp. OR exp Dogs/) AND (wound.mp. OR wounds.mp. OR (wounds and injuries).mp. OR abrasion.mp. OR abrasions.mp. OR laceration.mp. OR lacerations.mp. OR lesion.mp. OR lesions.mp. OR exp Wounds and Injuries/ OR exp Lacerations/) AND (honey.mp. OR Manuka.mp. OR Medihoney.mp. OR Activon.mp. OR exp Honey/) were used in a Medline search, using the Ovid interface. In addition, the search terms (dog.mp. OR dogs.mp. OR canine.mp. OR canines.mp. OR Canis.mp. OR exp Dogs/ OR exp Canis/) AND (wound.mp. OR wounds.mp. OR (wounds and injuries).mp. OR abrasion.mp. OR abrasions.mp. OR laceration.mp. OR lacerations.mp. OR lesion.mp. OR lesions.mp. OR exp wounds/ OR exp injuries/ OR exp abrasion/ OR exp lesions/) AND (honey.mp. OR Manuka.mp. OR Medihoney.mp. OR Activon.mp. OR exp honey/) were used in a CAB Abstracts search, also using the Ovid interface. Seven papers were found in the Medline search. Search last performed: 28 May 2020 No evidence base was found to answer this question. All papers returned by the search were excluded because they either did not directly compare dressings with honey to dressings alone, were related to in vitro and experimental research or were narrative review articles or conference proceedings. The search strategy used in this evidence evaluation was not designed to include wounds resulting from burns. A search with terms relating to burns would, therefore, need to be carried out to address the role of manuka honey in burn wound healing. This evidence evaluation highlights that there is a pressing need for further research to directly compare the success rates of the different wound management approaches. Although there were publications found that described the use of honey in wound management, these were primarily case reports – and therefore couldn't answer the comparative component of our question – or opinion pieces. Narrative reviews and expert opinion pieces, along with other sources such as textbooks and reputable online resources, can be used when deciding which approach to employ when no peer-reviewed evidence exists. However, an awareness of the strengths and limitations of all of these evidence sources in relation to decision making is important. Critically Appraised Topics (CATs) are a standardised, succinct summary of research evidence organised around a clinical question, and a form of evidence synthesis used in the practice of evidence-based medicine (EBM) and evidence-based veterinary medicine (EBVM). Access to CATs enables clinicians to incorporate evidence from the scientific literature into clinical practice. CATs will be published regularly in the Clinical Decision Making section of Vet Record.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.069
GPT teacher head0.289
Teacher spread0.220 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2020
Admission routes1
Has abstractyes

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