A Review of COVID-19 Chilblains-like Lesions and Their Differential Diagnoses
Bibliographic record
Abstract
ABSTRACT This review article focuses on the pathogenesis, clinical features, and diagnostic testing of the common pathologies that can manifest as chilblains-like lesions. These differentials include “COVID toes,” Raynaud phenomenon, acrocyanosis, critical limb ischemia, thromboangiitis obliterans, chilblains associated with lupus erythematosus, and idiopathic chilblains. The authors present a helpful mnemonic, ARCTIC, to assist clinicians in recognition and diagnosis. GENERAL PURPOSE To familiarize wound care practitioners with the differential diagnoses of chilblains-like lesions that could be associated with the complications of COVID-19. TARGET AUDIENCE This continuing education activity is intended for physicians, physician assistants, nurse practitioners, and nurses with an interest in skin and wound care. LEARNING OBJECTIVES/OUTCOMES After participating in this educational activity, the participant will: 1. Identify the population most often affected by COVID toes. 2. Select the assessments that help differentiate the various conditions that cause chilblains-like lesions. 3. Choose appropriate treatment options for the various conditions that cause chilblains-like lesions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".