Fluorescence imaging guided dressing change frequency during negative pressure wound therapy: a case series
Bibliographic record
Abstract
OBJECTIVE: Knowledge of wound bioburden can guide selection of therapies, for example, the use of negative pressure wound therapy (NPWT) devices with instillation in a heavily contaminated wound. Wound and periwound bacteria can be visualised in real-time using a novel, non-contact, handheld fluorescence imaging device that emits a safe violet light. This device was used to monitor bacterial burden in patients undergoing NPWT. METHODS: Diverse wounds undergoing NPWT were imaged for bacterial (red or cyan) fluorescence as part of routine wound assessments. RESULTS: We assessed 11 wounds undergoing NPWT. Bacterial fluorescence was detected under sealed, optically-transparent (routine) adhesive before dressing changes, on foam dressings, within the wound bed, and on periwound tissues. Bacterial visualisation in real-time helped to guide: (1) bioburden-based, personalised treatment regimens, (2) clinician selection of NPWT, with or without instillation of wound cleansers, and (3) the extent and location of wound cleaning during dressing changes. The ability to visualise bacteria before removal of dressings led to expedited dressing changes when heavy bioburden was detected and postponement of dressing changes for 24 hours when red fluorescence was not observed, avoiding unnecessary disturbance of the wound bed. CONCLUSION: Fluorescence imaging of bacteria prompted and helped guide the timing of dressing changes, the extent of wound cleaning, and selection of the appropriate and most cost-effective NPWT (standard versus instillation). These results highlight the capability of bacterial fluorescence imaging to provide invaluable real-time information on a wound's bioburden, contributing to clinician treatment decisions in cases where bacterial contamination could impede wound healing.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".