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Record W3007264226 · doi:10.1016/j.kisu.2019.11.010

Global case studies for chronic kidney disease/end-stage kidney disease care

2020· review· en· W3007264226 on OpenAlexaff
Chih‐Wei Yang, David C.H. Harris, Valérie A. Luyckx, Masaomi Nangaku, Fan Fan Hou, Guillermo García-García, Hasan Abu-Aisha, Abdou Niang, Laura Solá, Sakarn Bunnag, Somchai Eiam‐Ong, Kriang Tungsanga, Marie Richards, Nick Richards, Bak Leong Goh, Gavin Dreyer, Rhys Evans, Henry Mzingajira, Ahmed Twahir, Mignon McCulloch, Curie Ahn, Charlotte Osafo, Hsiang‐Hao Hsu, Lianne Barnieh, Jo‐Ann Donner, Marcello Tonelli

Bibliographic record

VenueKidney International Supplements · 2020
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of Calgary
FundersPan American Health OrganizationInternational Society of NephrologyWorld Health Organization
KeywordsKidney diseaseContext (archaeology)End stage renal diseaseIntensive care medicineMedicineDialysisKidney transplantationHealth careDiseaseScope (computer science)Economic growthTransplantationDevelopment economicsBusinessEconomicsPathologyGeographyInternal 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)0.003
metaresearch head score (Gemma)0.009
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 score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.009
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.047
GPT teacher head0.398
Teacher spread0.351 · 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

Citations116
Published2020
Admission routes1
Has abstractno

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