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Record W4394580052 · doi:10.1016/j.kisu.2024.01.007

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

2024· review· en· W4394580052 on OpenAlexafffund
Eranga Wijewickrama, Muhammad Rafiqul Alam, Divya Bajpai, Smita Divyaveer, Arpana Iyengar, Vivek Kumar, Ahad Qayyum, Shankar Prasad Yadav, Manjusha Yadla, 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, Dibya Singh Shah, Narayan Prasad, Anil Agarwal, Ejaz Ahmed, Suceena Alexander, Atefeh Amouzegar, Urmila Anandh, Shyam Bihari Bansal, Pramod Kumar Chhetri, Yeoungjee Cho, Ugyen Choden, Nizamuddin Chowdury, Arvind Conjeevaram, Mogamat Razeen Davids, Sara N. Davison, Hassane M. Diongole, Udeme E. Ekrikpo, Isabelle Éthier, Edwin Fernando Mervin, Winston Wing‐Shing Fung, Reena Rachel George, Anukul Ghimire, Gopal Basu, Swarnalatha Guditi, Chula Herath, Ghenette Houston, Htay Htay, Kwaifa Salihu Ibrahim, Georgina Irish, Kailash Jindal, Ahmad Baseer Kaihan, Shubharthi Kar, Tasnuva Sarah Kashem, Dearbhla Kelly, Asia Khanam, Vijay Kher, Rowena Lalji, Sandeep Mahajan, Aisha M. Nalado, Rubina Naqvi, K.S. Nayak, Brendon L. Neuen, Timothy O. Olanrewaju, Mohamed A. Osman, Sreejith Parameswaran, Klara Paudel, Анна Петрова, Harun Ur Rashid, Parnian Riaz, Syed Saad, Manisha Sahay, Aminu Muhammad Sakajiki, Emily See, Mythri Shankar, Ajay P. Sharma, Sourabh Sharma, Ibrahim Shiham, Geetika Singh, Stephen M. Sozio, Sophanny Tiv, Mayuri Trivedi, Somkanya Tungsanga, Andrea K. Viecelli, Marina Wainstein, Abdul Wazil, Dilushi Wijayaratne, Emily K. Yeung, Deenaz Zaidi

Bibliographic record

VenueKidney International Supplements · 2024
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of British ColumbiaUniversity of CalgaryUniversity of Alberta
FundersSchulich School of Medicine and DentistryFaculty of Medicine, Chulalongkorn UniversityCanadian Institutes of Health ResearchJohns Hopkins Bloomberg School of Public HealthGeorge Institute for Global HealthUniversity of ColomboAlberta InnovatesChulalongkorn UniversityUniversity of AlbertaInternational Society of NephrologyMcMaster UniversityLondon Health Sciences CentreJohns Hopkins UniversityPfizerFaculty of Medicine and Dentistry, University of AlbertaAstraZenecaAlberta Health ServicesUniversity of OttawaSchulich School of Medicine and Dentistry, Western UniversityUniversiteit UtrechtSanofiAlexion Pharmaceuticals
KeywordsKidney diseaseMedicineNephrologyPublic healthHealth carePeritoneal dialysisGlobal healthEnvironmental healthIntensive care medicineEconomic growthInternal medicineNursing

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)0.002
metaresearch head score (Gemma)0.003
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: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.005
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.056
GPT teacher head0.359
Teacher spread0.303 · 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
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

Citations13
Published2024
Admission routes2
Has abstractno

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