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Record W4281653192 · doi:10.2215/cjn.16341221

Variation in Peritoneal Dialysis Time on Therapy by Country

2022· article· en· W4281653192 on OpenAlexafffundabout
Mark Lambie, Junhui Zhao, Keith McCullough, Simon Davies, Hideki Kawanishi, David W. Johnson, James A. Sloand, Mauricio Sanabria, Talerngsak Kanjanabuch, Yong-Lim Kim, Jenny I. Shen, Ronald L. Pisoni, Bruce Robinson, Jeffrey Perl

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2022
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsSt. Michael's Hospital
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Health and Medical Research CouncilBaxter InternationalNational Research Council of ThailandCanadian Institutes of Health ResearchNational Institute for Health and Care ResearchKidney Research UKPatient-Centered Outcomes Research Institute
KeywordsMedicinePeritoneal dialysisHemodialysisInterquartile rangeTransplantationDialysisKidney transplantationHazard ratioContext (archaeology)Renal replacement therapyInternal medicineHome hemodialysisIntensive care medicineSurgeryConfidence interval

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Quantifying contemporary peritoneal dialysis time on therapy is important for patients and providers. We describe time on peritoneal dialysis in the context of outcomes of hemodialysis transfer, death, and kidney transplantation on the basis of the multinational, observational Peritoneal Dialysis Outcomes and Practice Patterns Study (PDOPPS) from 2014 to 2017. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Among 218 randomly selected peritoneal dialysis facilities (7121 patients) in the PDOPPS from Australia/New Zealand, Canada, Japan, Thailand, the United Kingdom, and the United States, we calculated the cumulative incidence from peritoneal dialysis start to hemodialysis transfer, death, or kidney transplantation over 5 years and adjusted hazard ratios for patient and facility factors associated with death and hemodialysis transfer. RESULTS: Median time on peritoneal dialysis ranged from 1.7 (interquartile range, 0.8-2.9; the United Kingdom) to 3.2 (interquartile range, 1.5-6.0; Japan) years and was longer with lower kidney transplantation rates (range: 32% [the United Kingdom] to 2% [Japan and Thailand] over 3 years). Adjusted hemodialysis transfer risk was lowest in Thailand, but death risk was higher in Thailand and the United States compared with most countries. Infection was the leading cause of hemodialysis transfer, with higher hemodialysis transfer risks seen in patients having psychiatric disorder history or elevated body mass index. The proportion of patients with total weekly Kt/V ≥1.7 at a facility was not associated with death or hemodialysis transfer. CONCLUSIONS: Countries in the PDOPPS with higher rates of kidney transplantation tended to have shorter median times on peritoneal dialysis. Identification of infection as a leading cause of hemodialysis transfer and patient and facility factors associated with the risk of hemodialysis transfer can facilitate interventions to reduce these events. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_05_31_CJN16341221.mp3.

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 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)0.001
metaresearch head score (Gemma)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.020
GPT teacher head0.327
Teacher spread0.307 · 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

Citations53
Published2022
Admission routes3
Has abstractyes

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