Digital Gangrene in a Patient with Systemic Lupus Erythematosus and Systemic Sclerosis: Figure 1.
Bibliographic record
Abstract
Nearly half of patients with systemic sclerosis (SSc) experience a digital ulcer, and many of these ulcers may progress to digital gangrene. Gangrene can stem from inadequate healing of digital ulcers or complications of comorbidities along with elevated C-reactive protein (CRP) levels. A 49-year-old woman diagnosed with systemic lupus erythematosus (SLE) and an overlap with SSc since 2006 presented in December 2010 with a 1-day history of acute pain and discoloration of all her digits (Figure 1). Figure 1. Dorsal and palmar views showing acute gangrene of all 5 digits on the right hand and chronic ulcers affecting the palmar aspect on the left hand (A, B). Panels (C) and (D) show both hands after 6 months of followup. Note the extensive skin desquamation on both hands. Her connective tissue disease course was characterized by sclerodactyly, gastrointestinal reflux, inflammatory arthritis, serositis, oral ulcers, Raynaud’s phenomenon (RP), positive antinuclear antibody with titer 1:640 in a speckled pattern, rheumatoid factor, anti-Sm, and anti-RNP antibodies. She was treated since 2008 with stable doses of methotrexate and leflunomide. She was not known to have any risk factors for atherosclerosis such as diabetes mellitus, hypertension, or dyslipidemia, and she did …
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".