Eruptive Disseminated Pyogenic Granulomas following Lightning Injury
Bibliographic record
Abstract
BACKGROUND: Pyogenic granuloma (PG) is a common benign acquired vascular tumor. It classically presents as a solitary friable nodule on the face or distal extremities. Disseminated eruption is rare and can occur spontaneously or secondary to various triggers, including burn injury. To date, the literature reports only 13 cases of eruptive PGs following burn injury, most from exposure to boiling milk or water. We report the first case of disseminated eruptive PGs following a lightning injury. CASE: A 17-year-old previously healthy boy developed second- and third-degree burns following lightning injury. Two weeks later, he developed widespread dark-purple polypoid exophytic tumors ranging from 1 to 10 cm in diameter extending beyond the limits of the initial burn injury. The lesions were friable and often formed erosions and crusts. The patient was otherwise well and laboratory and microbiological investigations were normal. Excisional biopsy of a lesion was diagnostic of PG and the patient was treated with surgical excision of the lesions, without recurrence. CONCLUSION: The exact pathogenesis of multiple PGs remains unknown. Several pathogenic mechanisms have been suggested, including production of angiogenic factors that stimulate endothelial proliferation and formation of minute arteriovenous fistulas by trauma.
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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 | 0.002 |
| 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 | 0.001 |
| 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 | 0.000 |
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".