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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 OpenAlexaff
Sayali Thakare, Paolo Nikolai So, Sonia Rodriguez, Mohamed Hassanein, Edgar V. Lerma, Nasim Wiegley

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.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.002

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

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 designNot applicable
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

Citations10
Published2023
Admission routes1
Has abstractyes

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