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Capacity for the management of kidney failure in the International Society of Nephrology Eastern and Central Europe region: report from the 2023 ISN Global Kidney Health Atlas (ISN-GKHA)
Caner Alparslan, Jolanta Małyszko, Fergus Caskey, Mirna Alečković-Halilović, Zdenka Hrušková, Silvia Arruebo, Aminu K. Bello, Sandrine Damster, Jo‐Ann Donner, Vivekanand Jha, David W. Johnson, Adeera Levin, Charu Malik, Masaomi Nangaku, Ikechi G. Okpechi, Marcello Tonelli, Feng Ye, Vladimı́r Tesař, Sanjin Rački, Atefeh Amouzegar, Zehra Aydın, Myftar Barbullushi, Sibel Gökçay Bek, Inga Arūnė Bumblytė, Yeoungjee Cho, Mogamat Razeen Davids, Sara N. Davison, Constantinos Deltas, Hassane M. Diongole, Smita Divyaveer, Udeme E. Ekrikpo, Isabelle Éthier, Agnes B. Fogo, Winston Wing‐Shing Fung, Anukul Ghimire, Eva Honsová, Ghenette Houston, Htay Htay, Kwaifa Salihu Ibrahim, Georgina Irish, Kailash Jindal, Rümeyza Kazancıoğlu, Dearbhla Kelly, Magdalena Krajewska, Mario Laganović, Rowena Lalji, Aisha M. Nalado, Radomir Naumović, Brendon L. Neuen, Milena Nikolova‐Vlahova, Ionuţ Nistor, Timothy O. Olanrewaju, Mohamed A. Osman, Mai Ots-Rosenberg, Анна Петрова, Ľudmila Podracká, Halima Resić, Parnian Riaz, László Rosivall, Syed Saad, Aminu Muhammad Sakajiki, Emily See, Mehmet Şükrü Sever, Stephen M. Sozio, Goce Spasovski, Sophanny Tiv, Serhan Tuğlular, Somkanya Tungsanga, Andrea K. Viecelli, Marina Wainstein, Emily K. Yeung, Deenaz Zaidi
Bibliographic record
VenueKidney International Supplements · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsUniversity of British ColumbiaUniversity of CalgaryUniversity of Alberta
FundersGeorge Institute for Global HealthCanadian Institutes of Health ResearchBezmialem Vakıf ÜniversitesiFaculty of Medicine and Dentistry, University of AlbertaChinese University of Hong KongUniversiteit StellenboschChulalongkorn UniversityUniversity of OxfordSemmelweis EgyetemDuke-NUS Medical SchoolUniversiteit UtrechtNile UniversityFaculty of Health and Medical Sciences, University of Western AustraliaRoyal Adelaide HospitalSingapore General HospitalFaculty of Medicine, Chulalongkorn UniversityPostgraduate Institute of Medical Education and Research, ChandigarhUniversity of QueenslandVanderbilt University Medical CenterKocaeli ÜniversitesiUniversity of AlbertaUniverzita Komenského v BratislaveUniversity of CyprusLietuvos Sveikatos Mokslų UniversitetasUniversity of OttawaMcMaster UniversityJohns Hopkins Bloomberg School of Public HealthVanderbilt UniversityUniversité de MontréalUniverzita Karlova v PrazeIran University of Medical SciencesAlexion PharmaceuticalsAlberta InnovatesAlberta Health ServicesAstraZenecaMarmara ÜniversitesiAmgenJohns Hopkins University
KeywordsNephrologyKidney diseaseMedicineHealth careGlobal healthRenal replacement therapyInternal medicineFamily medicineEconomic growthPublic healthIntensive care medicineNursingEconomics
Abstract
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score0.995
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 head0.045
GPT teacher head0.286
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary
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