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Record W2597551762 · doi:10.3747/pdi.2016.00057

Peritoneal Dialysis: A Scoping Review of Strategies to Maximize pd Utilization

2017· review· en· W2597551762 on OpenAlexaff
Bikaramjit Mann, Braden Manns, Lianne Barnieh, Matthew J. Oliver, Daniel J. Devoe, D. Lorenzetti, Robert P. Pauly, Robert R. Quinn

Bibliographic record

VenuePeritoneal Dialysis International · 2017
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of AlbertaHealth Sciences CentreSunnybrook Health Science CentreAlberta Bible CollegeUniversity of TorontoAlberta Kidney Disease NetworkUniversity of Calgary
Fundersnot available
KeywordsPeritoneal dialysisMedicineMEDLINEIntensive care medicineSystematic reviewDialysisEnd stage renal diseaseDiseaseSurgeryInternal medicine

Abstract

fetched live from OpenAlex

The percentage of end-stage renal disease (ESRD) patients treated with peritoneal dialysis (PD) has declined in many countries since the mid-1990s. Barriers to PD have been reviewed extensively in the literature, but evidence about strategies to address these barriers and maximize the safe and effective use of PD is lacking. We therefore decided to conduct a scoping review identifying strategies to maximize PD use in adults with ESRD. Our search strategy included the following online databases: MEDLINE (OVID), EMBASE, PubMed, Cochrane Controlled Trials Register, Current Controlled Trials, and Cochrane Database of Systematic Reviews for articles published from 1974 to November 2013. Experts in the field were contacted for information about other ongoing or unpublished studies. A complementary search was conducted in the gray literature. Websites of national, provincial or regional agencies were searched for documents regarding policies surrounding the use of PD. Individual dialysis centers need to identify barriers to increasing PD in their program and direct targeted strategies to maximize PD utilization. Our review highlights some effective strategies that may be used. Our review also highlights the need for further research into strategies to maximize PD utilization.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.789
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.166
GPT teacher head0.461
Teacher spread0.295 · 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 designOther design
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

Citations22
Published2017
Admission routes1
Has abstractyes

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