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Record W4394580193 · doi:10.1016/j.kisu.2024.01.009

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

2024· article· en· W4394580193 on OpenAlexafffund
Sabine Karam, Atefeh Amouzegar, Iman Alshamsi, Saeed M.G. Al Ghamdi, Siddiq Anwar, Mohammad Ghnaimat, Bassam Saeed, 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, Ali K. Abu‐Alfa, Shokoufeh Savaj, Pauline Abou-Jaoudeh, Turki Al Hussain, Issa Al Salmi, Mona Alrukhaimi, Anas Alyousef, Sola Aoun Bahous, Guangyan Cai, Hicham I. Cheikh Hassan, Yeoungjee Cho, Mogamat Razeen Davids, Sara N. Davison, Hassane M. Diongole, Smita Divyaveer, Udeme E. Ekrikpo, Isabelle Éthier, Winston Wing‐Shing Fung, Anukul Ghimire, Nakysa Hooman, Ghenette Houston, Htay Htay, Kwaifa Salihu Ibrahim, Georgina Irish, Kailash Jindal, Dearbhla Kelly, Rowena Lalji, Ahmed Mitwali, Mojgan Mortazavi, Aisha M. Nalado, Brendon L. Neuen, Timothy O. Olanrewaju, Mohamed A. Osman, Shahrzad Ossareh, Анна Петрова, Parnian Riaz, Syed Saad, Aminu Muhammad Sakajiki, Emily See, Stephen M. Sozio, Sophanny Tiv, Somkanya Tungsanga, Andrea K. Viecelli, Marina Wainstein, Hala Wannous, Emily K. Yeung, Deenaz Zaidi

Bibliographic record

VenueKidney International Supplements · 2024
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of British ColumbiaUniversity of CalgaryUniversity of Alberta
FundersFaculty of Medicine and Dentistry, University of AlbertaJohns Hopkins Bloomberg School of Public HealthGeorge Institute for Global HealthNile UniversityUniversité de MontréalChinese University of Hong KongIsfahan University of Medical SciencesFaculty of Health and Medical Sciences, University of Western AustraliaUniversity of OxfordInternational Society of NephrologyUniversity of AlbertaUniversiteit UtrechtIran University of Medical SciencesDuke-NUS Medical SchoolRoyal Adelaide HospitalUniversity of OttawaSingapore General HospitalMcMaster UniversityJohns Hopkins University
KeywordsMedicineKidney diseaseNephrologyKidney transplantationDialysisTransplantationPeritoneal dialysisInternal medicineHemodialysisPublic healthIntensive care medicineEnvironmental healthPathology

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 distilled prediction

Teacher imitation

Not 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 categoriesInsufficient payload (model declined to judge)
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 score0.721
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.310
Teacher spread0.266 · 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 teacher head, not a consensus.

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

Citations12
Published2024
Admission routes2
Has abstractno

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