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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.118

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
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