Fluorescence – modern method of the diagnosisof chronic wounds on the example of venous leg ulcerb
Bibliographic record
Abstract
Introduction: Fluorescence imaging has become a method for bacterial visualisation in chronic wounds for the last few years. MolecuLight i:X (MolecuLight, Inc, ON, Canada) is a handheld device, which enables quick diagnostics to determine both the type and location of pathogens present in the wound and on the skin. By means of fluorescent light illumination the tissues populated by pathogenic bacteria emit red or cyan fluorescent signatures, depending on the type of the pathogen: red fluorescence signal is emitted by Staphylococcus and Escherichia coli among others, while Pseudomonas aeruginosa produce cyan fluorescence. The fluorescence image also presents the spatial pattern of bacterial load, which creates bacterial mapping of the wound and may be used by a clinician for targeted sampling or debridement, among others. Aim: This study presents the method of microbiological fluorescent imaging and two case studies of patients with venous leg ulcers. Material and methods: In both cases, the sample for microbiological testing was obtained by means of a swab stick. Results: The results obtained from fluorescent imaging showed moderate-to-heavy bacterial load, which corresponded with the results from microbiology laboratory. Thanks to quick diagnostics with the use of MolecuLight i:X device, instant implementation of targeted topical actions such as wound hygiene, skin disinfection, appropriate dressing choice and curative treatment among others was possible. Conclusions: Our observations are consistent with the reports from other facilities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".