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Record W4394567966 · doi:10.1016/j.kisu.2024.02.001

Capacity for the management of kidney failure in the International Society of Nephrology North and East Asia region: report from the 2023 ISN Global Kidney Health Atlas (ISN-GKHA)

2024· article· en· W4394567966 on OpenAlexafffund
Winston Wing‐Shing Fung, Hyeong Cheon Park, Yosuke Hirakawa, Silvia Arruebo, Aminu K. Bello, Fergus Caskey, Sandrine Damster, Jo‐Ann Donner, Vivekanand Jha, David W. Johnson, Adeera Levin, Charu Malik, Masaomi Nangaku, Ikechi G. Okpechi, Marcello Tonelli, Seiji Ueda, Feng Ye, Yusuke Suzuki, Angela Yee‐Moon Wang, Atefeh Amouzegar, Guangyan Cai, Jer‐Ming Chang, Hung‐Chun Chen, Yuk Lun Cheng, Yeoungjee Cho, Mogamat Razeen Davids, Sara N. Davison, Hassane M. Diongole, Smita Divyaveer, Kent Doi, Udeme E. Ekrikpo, Isabelle Éthier, Kei Fukami, Anukul Ghimire, Ghenette Houston, Htay Htay, Kwaifa Salihu Ibrahim, Takahiro Imaizumi, Georgina Irish, Kailash Jindal, Naoki Kashihara, Dearbhla Kelly, Rowena Lalji, Bi-Cheng Liu, Shoichi Maruyama, Aisha M. Nalado, Brendon L. Neuen, Jing Nie, Akira Nishiyama, Timothy O. Olanrewaju, Mohamed A. Osman, Анна Петрова, Parnian Riaz, Syed Saad, Aminu Muhammad Sakajiki, Emily See, Stephen M. Sozio, Sydney Tang, Sophanny Tiv, Somkanya Tungsanga, Andrea K. Viecelli, Marina Wainstein, Motoko Yanagita, Chih‐Wei Yang, Jihyun Yang, Emily K. Yeung, Xueqing Yu, Deenaz Zaidi, Hong Zhang, Lili Zhou

Bibliographic record

VenueKidney International Supplements · 2024
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of British ColumbiaUniversity of CalgaryUniversity of Alberta
FundersCanadian Institutes of Health ResearchBayer YakuhinFaculty of Medicine and Dentistry, University of AlbertaOno PharmaceuticalMitsubishi Tanabe Pharma CorporationPeking UniversityAlberta Health ServicesAlberta InnovatesNile UniversityFaculty of Health and Medical Sciences, University of Western AustraliaSingapore General HospitalRoyal Adelaide HospitalKawasaki Medical SchoolTorii PharmaceuticalAmicus TherapeuticsUniversity of OxfordUniversité de MontréalPeking University First HospitalUniversity of AlbertaGeorge Institute for Global HealthInternational Society of NephrologyAlexion PharmaceuticalsSoutheast UniversityUniversiteit UtrechtDuke-NUS Medical SchoolJapan Society for the Promotion of ScienceGlaxoSmithKlineUniversity of OttawaMoonshot Research and Development ProgramSanofiMcMaster UniversityJapan Agency for Medical Research and DevelopmentCore Research for Evolutional Science and TechnologyAstraZeneca
KeywordsMedicineNephrologyKidney diseaseDialysisKidney transplantationPopulationPublic healthHealth careTransplantationInternal medicineIntensive care medicineFamily medicineEconomic growthEnvironmental healthNursing

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)0.009
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.093
Threshold uncertainty score0.185

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0100.003

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 head0.026
GPT teacher head0.301
Teacher spread0.275 · 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

Citations15
Published2024
Admission routes2
Has abstractno

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