Clinical indicators of wound infection and biofilm: reaching international consensus
Bibliographic record
Abstract
OBJECTIVE: To achieve international consensus relating to clinical indicators for a chronic wound, wound infection and biofilm presence to inform the development of international clinical guidance for assessing and managing wound infection. METHOD: An online Delphi consensus process of international key opinion leaders in infection was undertaken. A literature search underpinned the development of issue statements related to terminology, emerging topics and debate in the field of wound infection. Experts participated in three rounds of consensus voting, sharing their opinions and indicating their level of agreement with the issue statements. Votes were calculated using web-based software that implements a nominal group voting methodology previously published by Research and Development/University of California at Los Angeles. RESULTS: A total of 14 experts took part in the development process. Consensus was reached on clinical indicators of wound chronicity, wound infection and biofilm presence. Agreement was also reached that the term 'critical colonisation' should no longer be used to refer to a stage in the wound infection continuum. CONCLUSION: Outcomes from the consensus process were used to inform the development of international, evidence-informed guidance on the assessment and treatment of wound infection to promote improved outcomes for people with wounds.
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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 | 0.295 | 0.257 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".