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Record W1976058252 · doi:10.1186/ar3998

Nonlymphoma hematological malignancies in systemic lupus erythematosus

2012· article· en· W1976058252 on OpenAlexafffund
Mary Lu, Rosalind Ramsey‐Goldman, Sasha Bernatsky, Michelle Petri, S Manzi, Murray B. Urowitz, D. Gladman, Paul R. Fortin, Ellen M. Ginzler, Edward H. Yelin, DJ Wallace, Søren Jacobsen, MA Dooley, Christine Peschken, Alarcón Gs, Ola Nived, Lena Gottesman, Lindsey A. Criswell, Gunnar Sturfelt, Lene Dreyer, Jia-Lin Lee, AE Clarke

Bibliographic record

VenueArthritis Research & Therapy · 2012
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsUniversity of ManitobaUniversité LavalToronto Western HospitalMcGill University Health Centre
FundersCanadian Arthritis NetworkNational Institutes of HealthNational Institute of Arthritis and Musculoskeletal and Skin DiseasesArthritis SocietyLupus Research Alliance
KeywordsMedicineRheumatologyInternal medicineHematologyDermatologyOncology

Abstract

fetched live from OpenAlex

To describe nonlymphoma hematological malignancies in systemic lupus erythematosus (SLE). An international, multisite ( n = 28) SLE cohort was linked to regional tumor registries. We examined the types of nonlymphoma hematological cancers occurring after SLE diagnosis, and their demographic characteristics, including sex, race/ethnicity and age at time of cancer diagnosis. A total of 15,980 patients were observed for an average of 7.5 person-years. Of these, 90% were female and the majority was Caucasian. Based on age-matched general population cancer rates, the standardized incidence ratio for hematological cancers after SLE onset was 2.9 in females (95% CI = 2.3 to 3.6) and 3.6 in males (95% CI = 2.2 to 5.5). A total of 115 hematological cancers occurred: 82 were lymphoma (75 non-Hodgkin's, seven Hodgkin's), and 33 were nonlymphoma. Of the 33 nonlymphoma cases, 13 were of lymphoid lineage: multiple myeloma (MM, n = 5), plasmacytoma ( n = 3), B-cell chronic lymphocytic leukemia (B-CLL, n = 3), lymphocytic leukemia ( n = 1), and precursor cell lymphoblastic leukemia ( n = 1). The remaining 20 cases were of myeloid lineage: myelodysplastic syndrome (MDS, n = 7), acute myeloid leukemia (AML, n = 7), chronic myeloid leukemia (CML, n = 2), and four unspecified leukemias. All lymphoid malignancies occurred in female Caucasians, except for plasma cell neoplasms, where 4/5 MM cases and 1/3 plasmacytoma cases occurred in blacks (the others being Asian and Caucasian). At the time of MM diagnosis in SLE, the median age was 49 years (range 45 to 57), while for the three plasmacytoma SLE cases the median age was 35 years (range 25 to 62). In the female general population, median age at onset is 70 years for MM [ 1 ] and 55 years for plasmacytomas [ 2 ]. The median age of SLE subjects at B-CLL onset was 65 years (range 58 to 83), similar to the female general population (74 years). Of 20 myeloid malignancies, three (15%) occurred in males, and six of the 20 myeloid malignancies (30%) occurred in blacks. All seven AML cases were female, with median age at AML diagnosis of 48 years (range 34 to 72), versus 66 years in the female general population. The seven MDS cases (six females) occurred at a median age of 48 years (range 36 to 59), versus 76 years in the general population. The ages at time of diagnosis for the two CML cases (one female) were similar to the general population median (65 years). In our SLE cohort, the most common nonlymphoma hematological malignancies observed were myeloid types (MDS and AML). This is in contrast to the general population, where lymphoid types are three times more common than myeloid [ 3 ]. Most (80%) MM cases in our SLE cohort occurred in blacks. Most of our nonlymphoma hematological malignancy cases were younger than general population median age of onset, although this could simply reflect our cohort demographics.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.063
GPT teacher head0.346
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2012
Admission routes2
Has abstractyes

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