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Record W4380993043 · doi:10.1016/j.xkme.2023.100688

Novel Therapeutics for Management of Lupus Nephritis: What Is Next?

2023· article· en· W4380993043 on OpenAlex
Sayali Thakare, Paolo Nikolai So, Sonia Rodriguez, Mohamed Hassanein, Edgar V. Lerma, Nasim Wiegley

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueKidney Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsLupus nephritisMedicineSystemic lupus erythematosusIntensive care medicineNephrologyNephritisClinical trialImmunologyInternal medicineDisease

Abstract

fetched live from OpenAlex

Lupus nephritis is a severe, organ-threatening manifestation of systemic lupus erythematosus. The current standard of care in the treatment of lupus nephritis is limited to broad-spectrum immunosuppressants, which have significant concerns of short- and long-term toxicity. With traditional approaches, kidney survival and patient outcomes have remained suboptimal. Robust research in the therapeutics of lupus nephritis has resulted in development of many novel drugs targeting specific inflammatory response pathways. Some newer agents have shown a definitive signal of benefit when added to standard of care. With the advent of precision medicine in nephrology, lupus nephritis treatment may undergo a shift toward incorporating approaches using these newer drugs and individualizing care of our patients. This review highlights major advances in management of lupus nephritis over the last 25 years and explores the ongoing trials of emerging therapies in lupus nephritis.

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.

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.437
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.091
GPT teacher head0.368
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