The Improvement of Wound-Associated Pain and Healing Trajectory With a Comprehensive Foot and Leg Ulcer Care Model
Bibliographic record
Abstract
PURPOSE: Pain is a major concern for subjects with chronic wounds, but its optimal management remains elusive. The aim of this study was to validate an organized pain management approach using the Wound Associated Pain model in subjects with chronic leg and foot ulcers. DESIGN: We completed a prospective cohort study that documented pain in chronic wound subjects over a 4-week period. SUBJECTS AND SETTING: A total of 111 subjects with chronic leg and foot ulcers were recruited from the community and ambulatory wound care clinics. RESULTS: Using a systematic approach based on the Wound Associated Pain model, we demonstrated improved overall wound healing outcomes in 111 subjects with chronic leg and foot ulcers. Using an 11-point numerical rating scale, the average level of pain was reduced from 6.3 at week 0 to 2.8 at week 4 (P < .001). The average healing rate was 0.39 cm per week and the average relative reduction in size was 59.36% (t = 2.31; P = .023). To examine the relationship between pain and wound healing, pain levels were compared in subjects who achieved wound closure and those who did not. The mean pain score was 1.67 for the healed subjects in contrast to 3.21 for those who did not achieve complete wound closure (P < .041). CONCLUSIONS: A comprehensive patient assessment can improve chronic leg and foot ulcer wound-related pain and healing rates. The mean pain scores are lower for patients with healed ulcers than for those who do not obtain complete wound closure.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".