IWII Wound Infection in Clinical Practice consensus document: 2022 update
Bibliographic record
Abstract
ABSTRACT: Wound infection is a major challenge for clinicians globally, with accurate and timely identification of wound infection being critical to achieving clinical and cost-effective management, and promotion of healing. This paper presents an overview of the development of the International Wound Infection Institute (IWII)'s 2022 Wound Infection in Clinical Practice consensus document. The updated document summarises current evidence and provides multidisciplinary healthcare providers with effective guidance and support on terminology, paradigms related to biofilm, identification of wound infection, wound cleansing, debridement and antimicrobial stewardship. Integral to the update is revision of wound infection management strategies which are incorporated within the IWII's Wound Infection Continuum (IWII-WIC) and management plan. The aim of the 2022 IWII consensus document update was to provide an accessible and useful clinical resource in at least six languages, incorporating the latest evidence and current best practice for wound infection and prevention. Dissemination techniques for the consensus are discussed and highlighted.
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 | 0.055 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 0.022 |
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".