MétaCan
Menu
Back to cohort
Record W4394579968 · doi:10.1016/j.kisu.2024.01.005

Capacity for the management of kidney failure in the International Society of Nephrology Newly Independent States and Russia region: report from the 2023 ISN Global Kidney Health Atlas (ISN-GKHA)

2024· article· en· W4394579968 on OpenAlexafffund
Larisa Prikhodina, Kirill Komissarov, N. Bulanov, 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, Feng Ye, Abduzhappar Gaipov, Atefeh Amouzegar, Aiperi Asanbek kyzy, Yeoungjee Cho, Mogamat Razeen Davids, Sara N. Davison, Hassane M. Diongole, Smita Divyaveer, Udeme E. Ekrikpo, Isabelle Éthier, Winston Wing‐Shing Fung, Anukul Ghimire, Ghenette Houston, Htay Htay, Kwaifa Salihu Ibrahim, Georgina Irish, Д.Д. Іванов, Kailash Jindal, Dearbhla Kelly, Komiljon Khamzaev, Rowena Lalji, Aisha M. Nalado, Brendon L. Neuen, Timothy O. Olanrewaju, Mohamed A. Osman, Parnian Riaz, Syed Saad, Aminu Muhammad Sakajiki, Nora Sarishvili, Ashot Sarkissian, Emily See, Olimkhon Sharapov, Stephen M. Sozio, Irma Tchokhonelidze, Sophanny Tiv, Somkanya Tungsanga, Andrea K. Viecelli, Konstantin Vishnevskii, Olga Vorobyeva, Marina Wainstein, Emily K. Yeung, Deenaz Zaidi, Elena Zakharova

Bibliographic record

VenueKidney International Supplements · 2024
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsUniversity of British ColumbiaUniversity of CalgaryUniversity of Alberta
FundersDaiichi Sankyo EuropeChugai PharmaceuticalJohns Hopkins Bloomberg School of Public HealthUniversité de MontréalFresenius Medical Care North AmericaAstellas PharmaFaculty of Medicine and Dentistry, University of AlbertaEli Lilly and CompanyChinese University of Hong KongUniversiteit StellenboschChulalongkorn UniversityDuke-NUS Medical SchoolUniversiteit UtrechtAkebia TherapeuticsNile UniversityFaculty of Health and Medical Sciences, University of Western AustraliaRoyal Adelaide HospitalSingapore General HospitalUniversity of OxfordFaculty of Medicine, Chulalongkorn UniversityPostgraduate Institute of Medical Education and Research, ChandigarhUniversity of QueenslandUniversity of AlbertaGeorge Institute for Global HealthInternational Society of NephrologyGlaxoSmithKlineUniversity of OttawaBioCrystAstraZenecaHeart and Stroke Foundation of CanadaMcMaster UniversityJohns Hopkins UniversityAmgen
KeywordsNephrologyMedicineKidney diseaseDialysisKidney transplantationPopulationIntensive care medicineHealth careInternal medicineTransplantationFamily medicineEnvironmental healthEconomic growth

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)0.006
metaresearch head score (Gemma)0.012
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 score0.065
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.022
GPT teacher head0.308
Teacher spread0.286 · 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

Citations8
Published2024
Admission routes2
Has abstractno

Explore more

Same venueKidney International SupplementsSame topicOrgan Donation and TransplantationFrench-language works237,207