Practical treatment of wound pain and trauma: a patient-centered approach. An overview.
Bibliographic record
Abstract
Chronic wound pain is distressing and influences the patient's ability to function. One of the failures of modern medicine is the inadequate assessment and treatment of pain. The clinician's approach to chronic wound pain combines the "preparing the wound bed" paradigm with chronic wound pain models. A holistic approach must include the diagnosis and treatment of the underlying cause, identification and correction of patient-centered concerns, and the three major components of local wound care (debridement, bacterial balance/prolonged inflammation, and moisture balance). The Krasner pain model defines chronic (persistent), noncyclic acute, and cyclic acute wound pain. Chronic persistent wound pain without an event or trigger often relates to the cause of the wound that needs to be corrected to relieve the pain. Noncyclic acute pain is often experienced with a surgical procedure such as sharp debridement. Cyclical acute pain may occur repeatedly with removal or application of new local wound dressings. Securing a thorough pain history focusing on pain patterns will help healthcare professionals develop specific pain relief initiatives. Pain is a component of quality of life. Patient-centered concerns need to address pain control measures until the cause of the pain can be corrected. Controlling pain, however, may not always improve quality of life scores. Each of the components of local wound care also may be responsible for the production of pain; strategies need to be implemented to ensure adequate patient comfort.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".