MétaCan
Menu
Back to cohort
Record W4402487978 · doi:10.1016/j.kint.2024.07.035

Estimating glomerular filtration rate in kidney transplant recipients: considerations for selecting equations

2024· article· en· W4402487978 on OpenAlexfundno aff
Krishna A. Agarwal, Ogechi M. Adingwupu, Hocine Tighiouart, Shiyuan Miao, Marc Froissart, Michael Mauer, Wei Yang, Vicente E. Torres, Martin H. de Borst, Göran B. Klintmalm, Emilio D. Poggio, Peter Rossing, Ruben L. Velez, Anders Grubb, Andrew D. Rule, Ashtar Chami, Andrew S. Levey, Lesley A. Inker

Bibliographic record

VenueKidney International · 2024
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchClinical and Translational Science Collaborative of Cleveland, School of Medicine, Case Western Reserve UniversityMichigan Institute for Clinical and Health ResearchUniversity of California, San FranciscoUniversity of Illinois at Urbana-ChampaignNational Institutes of HealthVetenskapsrådetNational Center for Advancing Translational SciencesTufts Medical CenterSteno Diabetes Center CopenhagenPerelman School of Medicine, University of PennsylvaniaUniversity of PittsburghNational Institute of Diabetes and Digestive and Kidney DiseasesJohns Hopkins UniversityUniversity of MinnesotaNational Center for Research ResourcesUniversity of PennsylvaniaGeorgia Clinical and Translational Science AllianceDeutsches KrebsforschungszentrumKaiser PermanenteEmory UniversityUniversity of Kansas
KeywordsKidney transplantMedicineKidney transplantationUrologyRenal transplantKidneyIntensive care medicineMathematicsInternal medicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.032
metaresearch head score (Gemma)0.129
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.129
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0030.001
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.001

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 head0.028
GPT teacher head0.316
Teacher spread0.288 · 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 designTheoretical or conceptual
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

Citations7
Published2024
Admission routes1
Has abstractno

Explore more

Same venueKidney InternationalSame topicChronic Kidney Disease and DiabetesFrench-language works237,207