Preparing the wound bed 2003: focus on infection and inflammation.
Bibliographic record
Abstract
Wound bed preparation is the promotion of wound closure through diagnosis of the cause, attention to patient-centered concerns, and correction of systemic and local factors that may delay healing. To enhance the evidence base that may be lacking, a review of relevant literature was conducted and combined with input from the International Wound Bed Preparation Advisory Board and the Canadian Chronic Wound Advisory Board to create an updated examination of practices. A template based on expert opinion of the clinical actions corresponding to each step in the paradigm of preparing the wound bed is presented and the effects of local factors (tissue debridement, infection or inflammation, moisture balance, and edge effect [TIME]) are discussed. This review differentiates increased bacterial burden/infection in the superficial and deep wound bed compartments from inflammation and provides a topical approach to treatment. Inflammatory conditions causing leg ulcers, including pyoderma gangrenosum and vasculitis are reviewed. The topical combination of silver with absorptive dressings has led to new therapeutic options for increased bacterial burden in the surface wound compartment. A compilation of the available systematic reviews for the treatment of infection has been included as a background for the expert opinion.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".