Detection of bacteria in burn wounds with a novel handheld autofluorescence wound imaging device: a pilot study
Bibliographic record
Abstract
Objective: To compare the detection of bacteria in burn wounds between an bacterial fluorescence imaging device MolecuLight i:X, (Canada), and standard microbiological swabs. Methods: Wounds were swabbed three times on one occasion; once with a standard swab, once with a high-fluorescent area swab, indicating a bacterial load >104 colony-forming units (CFU)/gram and a finally with a non-fluorescent (nF) area swab. Proportion agreement of the microbiological results was calculated and the accuracy of the device to detect relevant bacteria was assessed. Results: A total of 14 patients with 20 wounds participated in the study. Median post-burn day at sampling time was 21 days. Of the 20 wounds, nine had a positive swab result in either of the three swabs, and 11 showed a highfluorescent area. Overall, positive and negative proportion agreement between standard swab and high-fluorescent swab sample results were 100%. Sensitivity, specificity, positive and negative predictive values of presence of high-fluorescence were 78%, 64%, 64%, and 78%, respectively. For Pseudomonas aeruginosa detection, these results were 100%, 70%, 44% and 100%, respectively. Conclusion: The diagnostic accuracy of the bacterial fluorescence imaging device to detect relevant bacteria in burn wounds was moderate and the reliability was equal to standard swabbing. Further research in larger sample sizes and on the relevance of minimal bacterial load and its potential to help with Pseudomonas aeruginosa management is needed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".