Biomodulation induced by fluorescent light energy versus standard of care in venous leg ulcers: a retrospective study
Bibliographic record
Abstract
OBJECTIVE: The recently completed EUREKA study confirmed the efficacy and safety profile of fluorescent light energy (FLE) in treating hard-to-heal wounds. To supplement the EUREKA prospective, observational, uncontrolled trial results, researchers selected one of the EUREKA clinical centres to conduct a retrospective analysis of matching wound care data for 46 venous leg ulcers (VLU) patients who had received standard wound care over a five-year period, compared with 10 EUREKA VLU subjects. METHOD: The study centre selected 46 patients with VLUs based on the matching criteria (wound age and size, patient's age and gender). They compared the healing rates of these matching VLUs with 10 VLU patients treated at the same centre during the EUREKA study. RESULTS: The EUREKA patients had larger and significantly older wounds (p<0.05) and significantly more risk factors (p<0.05) than the matching wounds. However, they had better outcomes (EUREKA: 40% versus matching group: 7% for full wound closure by 16 weeks). No wound breakdown was observed at 16 weeks in the EUREKA group, compared with 25% in the matching group. No EUREKA patient developed infections requiring antibiotics, compared with 37% in the matching group. EUREKA wounds had a mean relative wound area regression (RWAR) of 32% at week six and 50% at week 16, compared with -3% at week six and -6% at week 16 for the matching group. CONCLUSION: These findings show that the system based on FLE was well-tolerated and efficacious, with better clinical outcome results compared with the wounds analysed in this retrospective matching study and treated with standard of care alone.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".