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Record W4390533546 · doi:10.1016/j.kint.2023.09.002

KDIGO 2024 Clinical Practice Guideline for the management of LUPUS NEPHRITIS

2024· article· en· W4390533546 on OpenAlexaff
Brad H. Rovin, Isabelle Ayoub, Tak Mao Chan, Juan M. Mejía‐Vilet, Jürgen Floege

Bibliographic record

VenueKidney International · 2024
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsUniversity of Calgary
FundersNational Key Research and Development Program of China
KeywordsGuidelineLupus nephritisMedicineIntensive care medicineGrading (engineering)Randomized controlled trialClinical PracticeClinical trialEvidence-based medicineMEDLINECritical appraisalAlternative medicinePhysical therapyDiseasePathology

Abstract

fetched live from OpenAlex

This article is published as part of a supplement sponsored by Kidney Disease: Improving Global Outcomes (KDIGO). The opinions or views expressed in this supplement are those of the authors and do not necessarily reflect the opinions or recommendations of the International Society of Nephrology or Elsevier. Dosages, indications, and methods of use for products that are referred to in the supplement by the authors may reflect their clinical experience or may be derived from the professional literature or other clinical sources.

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)0.006
metaresearch head score (Gemma)0.018
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0060.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0040.002
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0090.013

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 head0.048
GPT teacher head0.435
Teacher spread0.387 · 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
GenreOther

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

Citations418
Published2024
Admission routes1
Has abstractyes

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