Evidence-based approach to manage persistent wound-related pain
Bibliographic record
Abstract
PURPOSE OF REVIEW: Pain is a significant concern in people with chronic wounds. A systematized approach is recommended for the management of wound-associated pain with the objectives to address pain relief, increase function, and restore overall quality of life. RECENT FINDINGS: Combinations of pharmacological agents are often recommended based on varying degree of pain severity, coexisting nociceptive and neuropathic pain, and chronic inflammation related to wound-associated pain. Topical agents including morphine, tricyclic antidepressants (e.g., amitriptyline), nonsteroidal anti-inflammatory drugs (NSAIDs), capsaicin, ketamine, and lidocaine/prilocaine provide pain relief with minimal side effects. Mindful dressing selection to minimize trauma, handle excess fluid, and prevent periwound skin damage has been shown to reduce persistent wound pain. To avoid nocebo hyperalgesia, it is important to address emotions, anticipation or negative expectation of discomfort. SUMMARY: Pain is a complex biopsychosocial phenomenon that requires multiple pharmacological and nonpharmacological management approach.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".