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Record W2625672208 · doi:10.1177/1203475417716366

Associated Hematolymphoid Malignancies in Patients With Lymphomatoid Papulosis: A Canadian Retrospective Study

2017· article· en· W2625672208 on OpenAlexaffabout
Mohanad AbuHilal, Scott Walsh, Neil H. Shear

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2017
Typearticle
Languageen
FieldMedicine
TopicCutaneous lymphoproliferative disorders research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLymphomatoid papulosisMedicineMalignancyDermatologyMycosis fungoidesLymphoproliferative disordersLymphomaPathologyCutaneous lymphoma

Abstract

fetched live from OpenAlex

BACKGROUND: Lymphomatoid papulosis is one of the primary cutaneous CD30+ T-cell lymphoproliferative disorders. Although considered a benign disease, lymphomatoid papulosis has been associated potentially with an increased risk of secondary hematolymphoid malignancies. OBJECTIVE: The aim of this study was to assess the clinical characteristics and histologic subtypes of lymphomatoid papulosis, identify the prevalence and types of secondary hematolymphoid malignancies, and determine the potential risk factors for development of these hematolymphoid malignancies. METHODS AND MATERIALS: A retrospective chart review was performed for all histologically confirmed cases of lymphomatoid papulosis between 1991 and 2016. RESULTS: Seventy patients with lymphomatoid papulosis were identified. Thirty patients (43%) experienced a secondary hematolymphoid malignancy. Twenty-four (80%) of the hematolymphoid malignancies occurred after the onset of lymphomatoid papulosis. Older age at diagnosis of lymphomatoid papulosis, male sex, histology type B, and the presence of T-cell receptor gene rearrangement are associated with higher risk of developing hematolymphoid malignancy. CONCLUSION: Lymphomatoid papulosis is associated with increased risk of developing secondary hematolymphoid malignancies, particularly mycosis fungoides and cutaneous anaplastic large cell lymphoma.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.019
GPT teacher head0.276
Teacher spread0.256 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations18
Published2017
Admission routes2
Has abstractyes

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