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Record W2184621642 · doi:10.1177/089686080802800606

Vascular Access Planning in Peritoneal Dialysis Patients

2008· article· en· W2184621642 on OpenAlexaff
Giusy Chiarelli, Monica Beaulieu, Mario Cozzolino, Mercedeh Kiaii, Paul A. Taylor, Adeera Levin, Diego Brancaccio, Maurizio Gallieni

Bibliographic record

VenuePeritoneal Dialysis International · 2008
Typearticle
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicinePeritoneal dialysisVascular accessHemodialysisIntensive care medicineArteriovenous fistulaDialysisRenal replacement therapyEnd stage renal diseaseSurgery

Abstract

fetched live from OpenAlex

Peritoneal dialysis (PD) is a well-established renal replacement therapy for end-stage renal disease patients. Nonetheless, on an annual basis, at least 10% of patients shift from PD to hemodialysis for a variety of reasons. Thus the issue of vascular access creation needs to be addressed for this small but significant group of patients. Despite the relatively consistent number of dropouts, the creation of an arteriovenous fistula prior to transfer remains suboptimal, and variable from center to center. Literature for this specific area is poor and dated. Guidelines seem to suggest vascular access creation in high-risk failure patients, but they have no detailed criteria to select patients that would likely fail PD and therefore take advantage of a backup access. There is a need to better understand and predict patients that require conversion to hemodialysis to develop a plan that focuses on wellness and maximum quality of life in the lifecycle of PD patients. This review addresses the issue of vascular access planning in adult PD patients, presents the available literature on the topic and the current guidelines and recommendations, and describes a research agenda to guide decision making in clinical practice.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.059
GPT teacher head0.383
Teacher spread0.325 · 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 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

Citations21
Published2008
Admission routes1
Has abstractyes

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