Best Practice Recommendations for Preparing the Wound Bed
Bibliographic record
Abstract
In Brief PURPOSE To provide the best available evidence in care of patients with chronic wounds. TARGET AUDIENCE This continuing education activity is intended for physicians, and nurses with an interest in wound care. OBJECTIVES After reading this article and taking this test, the reader should be able to: Identify local factors and recommendations for preparing the wound bed, including DIME and MEASURE, and ways to promote patient adherence to the treatment plan. Explain impairments and time guidelines to wound healing, problems with wound healing, and types of wound pain. Editor's note: This "Best Practice Recommendations" article is reprinted with permission from Wound Care Canada 2006;4(1):15-29. It is 1 of 4 articles published in 2006 following the latest Nursing Best Practice Guidelines from the Registered Nurses1 Association of Ontario (RNAO), which are updated approximately every 3 years. In this article, the concept of preparing the wound bed is updated to consider the whole patient before treating the wound. The evidence presented is connected to the RNAO's recommendations from its review of the literature up to the writing of its 2006 guidelines. This continuing education activity updates the concept of preparing the wound bed by considering the whole patient before treating the wound. Evidence identified by the Registered Nurses' Association of Ontario's Nursing Best Practice Guidelines is incorporated in this update.
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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 | 0.028 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".