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Record W2020415674 · doi:10.1097/mnh.0b013e32833d67a3

Peritoneal dialysis: an underutilized modality

2010· review· en· W2020415674 on OpenAlexaboutno aff
Sirin Jiwakanon, Yi-Wen Chiu, Kamyar Kalantar‐Zadeh, Rajnish Mehrotra

Bibliographic record

VenueCurrent Opinion in Nephrology & Hypertension · 2010
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsPeritoneal dialysisModality (human–computer interaction)MedicineDialysisTreatment modalityIntensive care medicineInternal medicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: There have been differential changes in outcomes of patients treated with in-center hemodialysis and peritoneal dialysis. In light of these changes, providers and practices should reevaluate the utilization of peritoneal dialysis. RECENT FINDINGS: Accumulating evidence confirms that the present distribution of dialysis modality in the United States does not reflect patient choice. Furthermore, in most recent cohorts, the 5-year adjusted survival of patients treated with hemodialysis and peritoneal dialysis is remarkably similar (35 and 33% respectively). Similar results have been reported from Canada, Australia, and New Zealand. Moreover, health-related quality of life of peritoneal dialysis patients are no different from that reported by those treated with nocturnal hemodialysis. Finally, an expansion of use of peritoneal dialysis for the treatment of end-stage renal disease makes economic sense for the taxpayers - the payors for dialysis services. SUMMARY: The improvement in outcomes of peritoneal dialysis patients makes a compelling argument for the expansion of the use of the therapy for the treatment of end-stage renal disease in the United States. We think that 20-40% of patients can be treated with peritoneal dialysis. However, any expansion in use should be done gradually and should include training healthcare providers while continuously monitoring patient outcomes.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0030.005
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.142
GPT teacher head0.404
Teacher spread0.263 · 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 designNot applicable
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

Citations39
Published2010
Admission routes1
Has abstractyes

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