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Record W2588556567 · doi:10.1093/ndt/gfw171.17

SP446THE PRESCRIPTION IN PERITONEAL DIALYSIS: INTERNATIONAL COMPARISON FROM THE PERITONEAL DIALYSIS OUTCOMES AND PRACTICE PATTERNS STUDY (PDOPPS)

2016· article· en· W2588556567 on OpenAlexaffabout
Simon Davies, Junhui Zhao, Brian Bieber, Jeffrey Perl, Martin Wilkie, Mark R. Marshall, Hideki Kawanishi, Francesca Tentori

Bibliographic record

VenueNephrology Dialysis Transplantation · 2016
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicinePeritoneal dialysisMedical prescriptionIntensive care medicineDialysisHemodialysisInternal medicinePharmacology

Abstract

fetched live from OpenAlex

Introduction and Aims: Large variability in peritoneal dialysis (PD) use and PD outcomes (e.g. technique survival) exists across countries. Along with differences in patients’ case mix, variation in PD practices is likely to contribute; however, little is known on this regional variation. Our study reports PD prescription among participants in PDOPPS, which is the largest international study of PD practices to date. Methods: The PDOPPS is a prospective cohort study of PD patients ongoing in Australia, Canada, Japan, the United Kingdom (UK), and the United States (US) in collaboration with the International Society of Peritoneal Dialysis (ISPD). Here we present preliminary baseline data on PD prescription among PDOPPS participants on manual (CAPD) and automated PD (APD) (n=1,306) by country. Data were not yet available in the UK. Results: Patient demographics and PD prescription in each country are shown (Table). CAPD was most common in Japan, while APD was predominant elsewhere. Among CAPD patients those in US were less likely to have 3 or fewer exchanges. In Australia icodextrin was always used for long exchanges, and less so elsewhere. In Japan, number of APD cycles, total prescribed volume and dwell volumes (both in CAPD and APD) were lower than in other countries. Low Kt/V was less common in the US and Australia, where 24 hours urine volume was highest.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.395
Teacher spread0.329 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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