EXERCISE INTERVENTION IN THE MANAGEMENT OF VENOUS LEG ULCERS: A REVIEW
Bibliographic record
Abstract
Venous leg ulcers (VLUs) are the most common etiology of lower extremity ulceration, with approximately 70 to 80 percent of leg ulcers developing from venous insufficiency or disease. The financial burden of VLUs on society, increase in prevalence with age, the chronic nature of the disorder, and high recidivism rate are, in part, the driving forces behind efforts to determine the most effective management options to achieve complete wound healing in a timely and efficient manner. Thus, the purpose of this paper is to describe the etiology, diagnosis, and management of VLUs, with a particular emphasis on exercise intervention. While medical treatment of VLUs include wound debridement, infection control, and proper wound dressing, compression therapy and elevation are the most common and effective treatment option for VLUs. When combined with routine compression therapy, exercise intervention (e.g., aerobic, resistance, and flexibility exercises) significantly improves the healing of VLUs in a cost efficient manner and should be strongly considered in the routine management of patients with VLUs. Article visualizations:
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".