Meeting the challenges of wound-associated pain: anticipatory pain, anxiety, stress, and wound healing.
Bibliographic record
Abstract
P ain is an unpleasant physical and emotional experience that plays a key role in the lives of people with chronic wounds. It is well documented that the majority of patients with chronic wounds suffer from moderate to severe pain for a protracted period of time with frequent exacerbations. Although pain often is associated with conditions intrinsic to underlying etiologies (eg, acute lipodermatosclerosis in venous leg ulcers, Charcot changes with diabetic foot ulcers), trauma (pressure, shear, and friction), chemical irritation, infection, or inflammation, spontaneous pain may occur due to sensitization of nerve fibers. In studies conducted during dressing changes, patients describe the most excruciating pain at dressing removal as aggressive adhesives are peeled away from fragile and damaged periwound skin. Increasing evidence also validates pain with wound cleansing, especially when abrasive materials or forceps are used to remove debris from the wound bed. To raise awareness and promote a systemic approach to managing pain, Woo and Sibbald developed a wound-associated pain (WAP) model that highlights three key components: the wound, the cause, and the patient (see Figure 1). First, the underlying cause of the wound-associated pain must be treated. Second, local wound care issues that may exacerbate woundassociated pain must be addressed. These include tissue trauma (dressing removal and wound cleansing); moisture balance (too much moisture can cause skin maceration and erosion while too little moisture dries out the dressing that then tends to adhere to wound bed); infection/inflammation (increased pain is a warning sign for potential deep wound infection); and patient-centered concerns (eg, anxiety, depression, anticipation of pain). Meeting the Challenges of Wound-associated Pain: Anticipatory Pain, Anxiety, Stress, and Wound Healing
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| 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".