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Record W3004696160 · doi:10.1177/0896860819895362

Incremental peritoneal dialysis

2020· article· en· W3004696160 on OpenAlexaff
Peter G. Blake, Jie Dong, Simon Davies

Bibliographic record

VenuePeritoneal Dialysis International · 2020
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsWestern University
Fundersnot available
KeywordsPeritoneal dialysisMedicineRenal functionUrologyObservational studyDialysisRandomized controlled trialClearanceIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Incremental peritoneal dialysis (PD) has been variably defined. It involves taking advantage of the residual renal function that is usually present at initiation of dialysis to initially prescribe less onerous lower doses of PD while still achieving individualized clearance goals. We propose that incremental PD be defined as a strategy, rather than a particular regime, in which: (1) less than standard "full-dose" PD is initially prescribed in recognition of the value of residual renal function; (2) peritoneal clearance is initially less than the individualized clearance goal but the combination of peritoneal plus renal clearance achieves or exceeds that goal clearance; and (3) there is a clear intention to increase dose of PD as renal clearance declines and/or symptoms appear. Incremental PD by its nature lessens the workload of dialysis for those doing PD, reduces cost and exposure of the peritoneal membrane to glucose, and may lessen mechanical symptoms. Evidence that incremental PD improves clinical outcomes compared to the use of full-dose PD is lacking but one randomized controlled trial, multiple observational studies, and a systematic review all suggest that outcomes are at least as good. Given that incremental PD costs less and is inherently less onerous, it is reasonable, pending larger randomized trials, to adopt this strategy.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.021
GPT teacher head0.280
Teacher spread0.259 · 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 designNot applicable
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

Citations81
Published2020
Admission routes1
Has abstractyes

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