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Record W3156392956 · doi:10.1016/j.ekir.2021.03.390

POS-372 A PRECISION MEDICINE APPROACH IDENTIFIES NONINVASIVE BIOMARKERS ASSOCIATED WITH INTRARENAL PATHWAY ACTIVATION IN PATIENTS WITH PROTEINURIC RENAL DISEASES

2021· article· en· W3156392956 on OpenAlexaff
Laura H. Mariani, Fadhl Alakwaa, Phillip J. McCown, Woong Ju, Jennifer L. Harder, Heather N. Reich, Felix Eichinger, Brad A. Godfrey, Vincent Boima, Adebowale Ademola, Jeffrey B. Hodgin, Akinlolu Ojo, Matthias Kretzler

Bibliographic record

VenueKidney International Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineInternal medicineOncologyBioinformatics

Abstract

fetched live from OpenAlex

The histopathology-based classification of FSGS and MCD does not capture the molecular basis of these diseases or predict response to therapy. Targeted treatment approaches (anti-TNF-alpha therapy, FONT trial, Trachtman, Am J Kidney Dis. 2010) led to remission induction in 25% of participating, unselected FSGS patients. We describe methods using transcriptomic and proteomic data to predict pathway activation in patients, which may be useful for targeted intervention trials in patients.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.007
GPT teacher head0.232
Teacher spread0.224 · 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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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Same venueKidney International ReportsSame topicRenal Diseases and GlomerulopathiesFrench-language works237,207