Fluorescent Imaging as a Component of Diagnosing Pyoderma Gangrenosum: A Case Report
Bibliographic record
Abstract
ABSTRACT: A 64-year-old White woman was admitted to the hospital with complaint of progressive right hip ulceration at the wound site following a total right hip arthroplasty. Initial history and physical examination gave a leading differential diagnosis of pyoderma gangrenosum. Until recently, the exclusion of infection for pyoderma gangrenosum has been largely clinical and supported by cultures/biopsies demonstrating the absence of infection. The MolecuLight i:X (MolecuLight, Toronto, Ontario, Canada) is a novel bedside fluorescent imaging device capable of determining the bacterial burden within a wound in real time. Fluorescent imaging excluded infection at the initial visit, and debridement was avoided. Subsequently, pathergy was avoided as well. The patient was started on topical clobetasol with hypochlorous acid-soaked dressings. She also received 80 mg daily of prednisone and high-dose vitamin D3 (10,000 IU). Recovery was complicated by a deep tunnel along the incisional line at 3 months postdiagnosis, which required slowing of the prednisone taper and the addition of colchicine. Repeat cultures grew Parvimonas, Pseudomonas, and Streptococcus species. Appropriate antibiotics were given. The patient was transitioned from prednisone to adalimumab and started on negative-pressure wound therapy. Negative-pressure wound therapy was discontinued at 5 months, and the wound resolved at 6 months.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| 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".