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Record W2261863140 · doi:10.1097/bor.0000000000000202

Malignancies in systemic lupus erythematosus

2015· review· en· W2261863140 on OpenAlexaff
G.C. Goobie, Sasha Bernatsky, Rosalind Ramsey‐Goldman, Ann E. Clarke

Bibliographic record

VenueCurrent Opinion in Rheumatology · 2015
Typereview
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsFoothills Medical CentreMcGill UniversityHealth Sciences CentreUniversity of Calgary
FundersNational Cancer Institute
KeywordsMedicineMalignancyProstate cancerLymphomaSystemic lupus erythematosusBreast cancerCancerImmunologyAutoantibodyLung cancerPopulationOncologyInternal medicineDiseaseAntibody

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Patients with systemic lupus erythematosus (SLE) have altered incidences of certain malignancies as compared with the general population. This review summarizes the recent literature on risk of malignancy in SLE and proposed mechanisms for these altered susceptibilities. RECENT FINDINGS: Recent studies have confirmed previous data showing an increased risk of hematological, lung, thyroid, liver, cervical and vulvovaginal cancers, while demonstrating a decreased risk of breast and prostate cancer. Lymphomagenesis in SLE has been linked to increased activity of multiple inflammatory cytokines as well as possible viral causes. The decreased rates of hormone-sensitive cancers, such as breast and prostate is speculated to be related to the presence of lupus autoantibodies and downregulation of certain proteins in SLE. This knowledge has been utilized to investigate new therapeutic modalities for these malignancies. SUMMARY: Recent data confirm previously reported altered malignancy rates in SLE. There has been some elucidation of mechanisms underlying cancer development in SLE, although additional work is yet to be done.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.483
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.147
GPT teacher head0.424
Teacher spread0.277 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations106
Published2015
Admission routes1
Has abstractyes

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