Improving the Quality of Chronic Wound Care Using an Advanced Wound Management Program and Gentian Violet/Methylene Blue-Impregnated Antibacterial (GV/MB) Dressings: A Retrospective Study.
Bibliographic record
Abstract
INTRODUCTION: Comprehensive wound management programs that employ a standardized integrated care bundle (ICB) and advanced wound dressings are generally recognized to decrease healing times and treatment costs. The purpose of this study was to compare wound healing rates and cost efficiencies as measured by nursing-care requirements for patients not on an ICB versus patients on an ICB and using a gentian violet/methylene blue-impregnated (GV/MB) antimicrobial advanced wound dressing. MATERIALS AND METHODS: The comprehensive wound management programs enabled continuous, standardized measurement of each patient's wound episode from admission with a wound to healing and discharge. Data was recorded over 24 months from 2016 to 2018. The variables recorded for each patient included: wound healing time (number of weeks), wound acuity based on the Bates-Jensen Wound Assessment Tool (BWAT), a comorbidity index (using the Charlson Comorbidity Index), and the number of wound dressing changes. The wound dressing changes required a visit by a registered nurse and, therefore, served as an indicator of care delivery costs where the dressing change visit cost was $68 (CAD). RESULTS: A total of 6300 patients (25% of the total study population) were identified as using GV/MB dressings within the context of an ICB. The mean healing time for these patients was accelerated more than 50% versus patients not on an ICB. The average total cost of patient care was reduced by more than 75% from diagnosis to wound healing when patients were on an ICB with GV/MB dressings. These results compared well to patients on ICBs that had other types of advanced dressings. CONCLUSION: The study demonstrates that a comprehensive wound management program based on integrated care bundles in conjunction with GV/MB dressings can be a highly-effective clinical option. The benefits showed significant reductions in healing times and treatment costs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".