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Record W1552899198 · doi:10.1161/circ.129.suppl_1.55

Abstract 55: Kidney Measures Beyond Conventional Risk Factors for Predicting Incident Cardiovascular Disease: A Collaborative Meta-Analysis of 16 Cohorts

2014· article· en· W1552899198 on OpenAlexaff
Kunihiro Matsushita, Josef Coresh, Yingying Sang, John Chalmers, Caroline S. Fox, Areef Ishani, Tazeen H. Jafar, Simerjot K Jassal, Gijs W.D. Landman, Paul Muntner, Paul Roderick, Toshimi Sairenchi, Ben Schöttker, Anoop Shankar, Michael G. Shlipak, Marcello Tonelli, John Townend, Arjan van Zuilen, Kazumasa Yamagishi, Kentaro Yamashita, Ron T. Gansevoort, Mark J. Sarnak, David G. Warnock, Mark Woodward, Johan Ärnlöv

Bibliographic record

VenueCirculation · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineKidney diseaseAlbuminuriaRenal functionPopulationInternal medicineCreatinineCardiologyEnvironmental health

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.048
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.048
Threshold uncertainty score0.256

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0480.063
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0140.059
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0030.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.038
GPT teacher head0.291
Teacher spread0.253 · 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 designMeta-analysis
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
Published2014
Admission routes1
Has abstractyes

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