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Record W4406760011 · doi:10.1002/adtp.202400502

Bee Better: The Role of Honey in Modern Wound Care

2025· article· en· W4406760011 on OpenAlexafffund
Léo‐Paul Tricou, Natalie Guirguis, Sarah Djebbar, B. Freedman, Simon Matoori

Bibliographic record

VenueAdvanced Therapeutics · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBee Products Chemical Analysis
Canadian institutionsUniversité de Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsHoney beeWound careMedicineIntensive care medicineBiologyBotany

Abstract

fetched live from OpenAlex

Abstract Honey has been used as an empirical wound care agent for thousands of years and continues to be investigated and used in chronic wound care. In the past few years, several commercially available medical grade honey‐based products have been approved for chronic wound therapy. Clinical trials showed that the therapeutic benefit of honey depends on wound type and honey composition. Recent insights into the pharmacology of honey in wound therapy over the past two decades have led to increased interest in this natural remedy and highlighted various antimicrobial and immunomodulatory properties that contribute to its pharmacologic action. However, the interaction between honey and the wound microenvironment on wound healing remains unclear. In this perspective, the current clinical evidence supporting the use of honey in wound care is presented and highlights its molecular mechanisms of action to eventually critically discuss the opportunities and challenges of using honey in wound care.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.008
GPT teacher head0.225
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations3
Published2025
Admission routes2
Has abstractyes

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