Bibliographic record
Abstract
Abstract This chapter reviews skin diseases and symptoms that develop secondarily to underlying malignant or benign diseases of the haematopoietic system. Skin lesions may be caused directly by infiltration of the skin with malignant cells (leukaemia cutis and lymphomatous infiltrates) or by paraproteins secreted by malignant plasma cells (amyloid light‐chain amyloidosis, type I cryoglobulinaemia and macroglobulinaemia cutis). A number of skin signs are an indirect effect of the underlying malignancy, such as paraneoplastic conditions or dermatological syndromes associated with haematological neoplasms (Schnitzler syndrome, POEMS, AESOP and TEMPI syndromes, neurofibromatosis, juvenile xanthogranuloma and juvenile myeloid leukaemia). Kikuchi–Fujimoto disease, Kimura disease, Rosai–Dorfman disease and IgG4‐related disease are idiopathic lymphoadenopathies presenting with characteristic skin lesions. Skin infections, eczematous lesions and vasculitis are the most common cutaneous signs of an underlying immunodeficiency. Chronic skin ulcers may accompany different haemoglobinopathies, such as thalassaemias and sickle cell anaemia. Finally, the skin signs of transfusion reactions (acute allergic reactions, transfusion‐associated graft‐versus‐host disease and post‐transfusion purpura) are reviewed. Diseases of the haematopoietic system often manifest themselves in the skin, and some manifestations are so typical that the dermatologist may be the first physician to detect the underlying blood disorder. More commonly, however, skin symptoms arise in the context of a known haematopoietic disease.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".