Healing Rates of Venous Leg Ulcers Managed With Compression Therapy
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to explore average time to heal for patients with venous leg ulcers (VLUs) receiving standard of care that included compression and advanced wound dressings. DESIGN: Secondary analysis of an existing electronic database. SUBJECT AND SETTINGS: A convenience sample consisting of 1323 patients with VLUs from various community care sectors (homecare and clinics) across Canada. METHODS: The Wound Studies database used in the analysis consisted of data from 6 studies conducted prospectively between 1999 and 2009 in which the treatment and delivery of care for all lower leg ulcers (venous, arterial, and mixed) in Canada was examined. From these studies, only patients with VLUs, with an ankle-brachial pressure index of greater than 0.8, and surface area measurements of the ulcers at baseline, 3 months, and 6 months were included. Descriptive statistics were used to determine the proportion of patients who achieved closure at 3 and 6 months and explore the weekly and monthly healing rates for those who did and did not achieve closure. Logistic regression analysis was performed to identify predictive factors for healing. RESULTS: A total of 777 patients (mean age 69 years) met inclusion criteria. The proportion of patients who achieved closure at 3 and 6 months was 42.2% and 48.6%, respectively. Of the participants who achieved wound closure, monthly mean healing rate, measured by percentage of reduction in surface area, was 33.4% (0.56 cm, SD 1.4 [median 0.15 cm]) through month 3, and 31.0% (0.70 cm, SD 1.6 [median 0.08 cm]) through month 6. The overall monthly surface area reduction was 30%. CONCLUSION: Study findings suggest a monthly surface area reduction of 30% provides a baseline healing rate for VLUs managed with compression therapy and advanced dressings. Findings also suggest standard of care is not sufficient for healing in over 50% of the population, as the proportion of those who achieved closure at 3 and 6 months was 42.2% and 48.6%, respectively.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".