A Scoping Review to Identify Clinical Signs, Symptoms and Biomarkers Reported in the Literature to Be Indicative of Biofilm in Chronic Wounds
Bibliographic record
Abstract
The objective of this review was to identify clinical signs/symptoms reported in the literature to be indicative of biofilm in chronic wounds. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews, and the Joanna Briggs Institute Evidence Synthesis manual guided review conduct. Any article/study type reporting signs/symptoms of biofilm in adults with venous, diabetic, pressure and/or mixed arterial/venous ulcers was eligible. Medline, Embase, CINAHL, Cochrane CENTRAL and the Bielefeld Academic Search Engine were searched. Titles/abstracts and full-text articles were screened against eligibility criteria. One-hundred and eleven reports of 109 articles were included. They provided 830 accounts of clinical signs/ symptoms being indicative of biofilm. These were categorised into 26 statements. Visual indicators such as a shiny, slimy layer on a non-healing wound surface quickly reforming in the absence of frequent cleansing or debridement represented 24% of accounts, followed by failed response to antimicrobial therapies (15%), and failure of wound to close or progress to healing despite optimal management strategies (13%). Wound duration > 6 weeks and extreme tolerance to host defences represented 1% of accounts. Clinical signs/symptoms are recommended and used as indicators of biofilm presence in chronic wounds but with little supporting validation data.
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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 | 0.019 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.031 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".