MétaCan
Menu
Back to cohort
Record W3158809461 · doi:10.1159/000515541

Tackling Dialysis Burden around the World: A Global Challenge

2021· article· en· W3158809461 on OpenAlexaffabout
Philip Kam‐Tao Li, Gordon Chun‐Kau Chan, Jianghua Chen, Hung‐Chun Chen, Yuk‐Lun Cheng, Stanley Fan, John Cijiang He, Weixin Hu, Wai-Hon Lim, York Pei, Boon Wee Teo, Ping Zhang, Xueqing Yu

Bibliographic record

VenueKidney Diseases · 2021
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersBarts Charity
KeywordsMedicineChinaPromotion (chess)Peritoneal dialysisIntensive care medicineDialysisEconomic growthKidney transplantationRenal replacement therapyKidney diseaseTransplantationFamily medicinePolitical scienceInternal medicineEconomics

Abstract

fetched live from OpenAlex

CKD is a global problem that causes significant burden to the healthcare system and the economy in addition to its impact on morbidity and mortality of patients. Around the world, in both developing and developed economies, the nephrologists and governments face the challenges of the need to provide a quality and cost-effective kidney replacement therapy for CKD patients when their kidneys fail. In December 2019, the 3rd International Congress of Chinese Nephrologists was held in Nanjing, China, and in the meeting, a symposium and roundtable discussion on how to deal with this CKD burden was held with opinion leaders from countries and regions around the world, including Australia, Canada, China, Hong Kong, Singapore, Taiwan, the UK, and the USA. The participants concluded that an integrated approach with early detection of CKD, prompt treatment to slow down progression, promotion of home-based dialysis therapy like peritoneal dialysis and home HD, together with promotion of kidney transplantation, are possible effective ways to combat this ongoing worldwide challenge.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.807
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.281
Teacher spread0.268 · 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
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

Citations48
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueKidney DiseasesSame topicDialysis and Renal Disease ManagementFrench-language works237,207