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Record W2226640202 · doi:10.3109/10428194.2015.1128542

Cutaneous involvement in multiple myeloma: a multi-institutional retrospective study of 53 patients

2016· article· en· W2226640202 on OpenAlexaff
Artur Jurczyszyn, Magdalena Olszewska‐Szopa, Vânia Hungria, Edvan Crusoé, Tomáš Pika, Michel Delforge, Xavier Leleu, Leo Rasche, Ajay K. Nooka, Agnieszka Druzd‐Sitek, Jan Walewski, Julio Dávila, Jo Caers, Vladimír Maisnar, Morie A. Gertz, Massimo Gentile, Dorotea Fantl, Giuseppe Mele, David H. Vesole, Andrew J. Yee, Chaim Shustik, Suzanne Lentzsch, Sonja Zweegman, Alessandro Gozzetti, Aleksander B. Skotnicki, Jorge J. Castillo

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2016
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsMcGill UniversityRoyal Victoria Hospital
Fundersnot available
KeywordsMedicineMultiple myelomaSkin biopsyBiopsyInfiltration (HVAC)ImmunohistochemistryPathologyRetrospective cohort studyInternal medicine

Abstract

fetched live from OpenAlex

Skin infiltration in multiple myeloma (skin MM) is a rare clinical problem. Only a few cases of skin involvement have been reported, primarily in single case reports. We analyzed and present the clinical outcomes, immunohistochemistry and cytogenetic features, and relevant laboratory data on 53 biopsy-proven skin MM cases. The median time from MM diagnosis to skin involvement was 2 years. There appears to be an overrepresentation of immunoglobulin class A (IgA) and light chain disease in skin MM. We found no correlation between CD56 negative MM and skin infiltration. We found that skin MM patients presented in all MM stages (i.e. ISS stages I to III), and there was no preferential cytogenetic abnormality. Patients with skin MM carry a very poor prognosis with a median overall survival (OS) of 8.5 months as time from skin involvement. Moreover, patients with IgA disease and plasmablastic morphology appear to have a worse OS.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.021
GPT teacher head0.262
Teacher spread0.242 · 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.

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

Citations41
Published2016
Admission routes1
Has abstractyes

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