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Record W2963756165 · doi:10.1002/9781118441213.rtd0149

The Skin and Disorders of the Haematopoietic and Immune Systems

2016· other· en· W2963756165 on OpenAlexaff
Robert Gniadecki

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineDermatologyDiseaseDyscrasiaPathologyCutisMalignancyImmunologyBone marrowPlasma cell

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.091
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.003
GPT teacher head0.212
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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