Improved detection of clinically relevant wound bacteria using autofluorescence image‐guided sampling in diabetic foot ulcers
Bibliographic record
Abstract
Clinical wound assessment involves microbiological swabbing of wounds to identify and quantify bacterial species, and to determine microbial susceptibility to antibiotics. The Levine swabbing technique may be suboptimal because it samples only the wound bed, missing other diagnostically relevant areas of the wound, which may contain clinically significant bacteria. Thus, there is a clinical need to improve the reliability of microbiological wound sampling. To address this, a handheld portable autofluorescence (AF) imaging device that detects bacteria in real time, without contrast agents, was developed. Here, we report the results of a clinical study evaluating the use of real-time AF imaging to visualise bacteria in and around the wound bed and to guide swabbing during the clinical assessment of diabetic foot ulcers, compared with the Levine technique. We investigated 33 diabetic foot ulcers (n = 31 patients) and found that AF imaging more accurately identified the presence of moderate and/or heavy bacterial load compared with the Levine technique (accuracy 78% versus 52%, P = 0·048; adjusted diagnostic odds ratio 7·67, P < 0·00022 versus 3·07, P = 0·066) and maximised the effectiveness of bacterial load sampling, with no significant impact on clinical workflow. AF imaging may help clinicians better identify the wound areas with clinically significant bacteria, and maximise sampling of treatment-relevant pathogens.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".