A Prospective Multisite Observational Study Incorporating Bacterial Fluorescence Information Into the UPPER/LOWER Wound Infection Checklists.
Bibliographic record
Abstract
INTRODUCTION: The UPPER/LOWER infection checklists look for signs and symptoms of local/superficial infection (UPPER) and deep infection (LOWER) to assist clinicians in identifying and distinguishing between these infection levels, facilitating appropriate treatment. The presence of 3 or more UPPER or LOWER criteria is indicative of infection. OBJECTIVE: This study evaluated the utility of incorporating real-time bacterial fluorescence imaging into the UPPER/LOWER checklists to enhance identification of infection in wounds. MATERIALS AND METHODS: This prospective, multisite study assessed 43 chronic wounds. Infection was identified in 27 wounds (62.8%) according to the UPPER/LOWER checklist criteria; 3 wounds were positive for both UPPER and LOWER infection, 1 wound was positive for LOWER infection only, and 23 wounds were positive for UPPER infection only. Fluorescence images were taken to detect wounds with high bacterial loads (> 104 CFU/g), indicated by the presence of red or cyan fluorescence. RESULTS: Red or cyan fluorescence from bacteria was observed in 88% of wounds (n = 38); all wounds positive for UPPER/LOWER were also positive for bacterial fluorescence. In 18 (41.9%) of the 43 wounds, fluorescence information added a third check to the UPPER/LOWER threshold, turning a negative diagnosis into a positive diagnosis of infection. Bacterial load was detected in 22/27 wounds swabbed, 17 of which exhibited heavy growth; in all wounds with detectable bacterial load, fluorescence signal was observed (positive predictive value = 100%, negative predictive value = 83%). Using microbiology as ground truth, inclusion of fluorescence information as an additional item in the checklists increased the sensitivity of the UPPER/LOWER checklist from 82% to 95%. CONCLUSIONS: These results suggest that the UPPER/LOWER checklist and fluorescence imaging work in a complementary manner to effectively identify wounds with high bacterial burden at the point-of-care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".