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Record W4413137398 · doi:10.2196/75475

Honey as a Wound Care Modality in Treating Deep Neck Space Abscesses: Protocol for a Randomized Controlled Trial

2025· article· en· W4413137398 on OpenAlexvenueno aff
Dian Paramita Wulandari, Yanri Wijayanti Subronto, Agus Surono

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldMedicine
TopicOtolaryngology and Infectious Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsRandomized controlled trialProtocol (science)MedicineModality (human–computer interaction)Wound careSurgeryPhysical therapyAlternative medicineArtificial intelligenceComputer sciencePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Deep neck abscesses are a disease in the field of otorhinolaryngology-head and neck surgery that causes significant morbidity, death, and expenditures. Treatment length, whether inpatient or outpatient, is also prolonged. Deep neck abscesses are managed with incision and drainage, abscess exploration, systemic broad-spectrum antibiotic treatment, comorbidity control, and postoperative wound care through recovery. Standard dressings for wound treatment have proven time-consuming and expensive. Honey is one type of dressing that has long been used in wound treatment for a number of body areas and disorders. OBJECTIVE: The purpose of this study is to investigate the use of honey as a potential substitute for standard dressings for deep neck abscesses. METHODS: This is a single-blind randomized controlled trial. Randomization will be done through simple random sampling. The population and sample of the study include all patients with deep neck abscesses treated at Dr Sardjito General Hospital, which is equipped with board-certified otorhinolaryngologists. Patients with deep neck abscesses who were given ethical clearance and meet the inclusion criteria were recruited as study participants until the sample size was reached. There are 18 participants in each group. Participants in the intervention group received standard dressings in addition to honey dressings, whereas those in the control group received standard dressings alone. Proinflammatory cytokines and growth factors, wound size, Bates-Jensen Wound Assessment Tools scoring, and quantitative bacterial colony identification will be all evaluated and assessed. The gathered data will be documented and subjected to statistical analysis. RESULTS: This manuscript presents a study protocol for a randomized controlled trial investigating honey as a wound care modality in patients with deep neck space abscesses. Ethical approval and partial funding for the study were obtained in May 2024. Recruitment and data collection commenced in June 2024 and were successfully completed as of June 2025. CONCLUSIONS: We hypothesize that honey may serve as a safe, effective, and affordable alternative wound dressing for the management of deep neck abscesses, with potential benefits in both clinical and health care system outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT06562257; https://clinicaltrials.gov/study/NCT06562257. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/75475.

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)0.034
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.071
Threshold uncertainty score0.239

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.031
Meta-epidemiology (narrow)0.0060.003
Meta-epidemiology (broad)0.0140.006
Bibliometrics0.0020.004
Science and technology studies0.0030.004
Scholarly communication0.0050.004
Open science0.0040.002
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0710.009

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 head0.079
GPT teacher head0.548
Teacher spread0.469 · 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 designRandomized trial
Domainnot available
GenreProtocol

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

Citations1
Published2025
Admission routes1
Has abstractyes

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