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Record W2315754259 · doi:10.3747/pdi.2014.00346

Selecting Peritoneal Dialysis in the Older Dialysis Population

2015· review· en· W2315754259 on OpenAlexaff
Matthew J. Oliver, Robert R. Quinn

Bibliographic record

VenuePeritoneal Dialysis International · 2015
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of CalgaryUniversity of Toronto
Fundersnot available
KeywordsPeritoneal dialysisMedicineHemodialysisIntensive care medicineDialysisPopulationPalliative careInternal medicineNursing

Abstract

fetched live from OpenAlex

Selecting peritoneal dialysis (PD) in older individuals is a complex, multi-step process. At each step, older individuals may not have the opportunity to receive PD unless care is optimized. Older individuals are less likely to complete a PD assessment, because of unstable medical conditions, consideration of palliative care, or reluctance to approach frail patients once they are established on hemodialysis (HD). Older individuals are also more likely to have medical or social conditions that contraindicate PD or to have barriers to self-care PD that may not be overcome with support. Older individuals who are eligible for PD may be reluctant to choose it, leaving HD as the default modality. Finally, receipt of PD may be compromised by urgent HD or PD catheter-related complications at the time of insertion. Despite all these challenges, older patients can do very well on PD, so each step should be well understood and optimized in renal programs to maximize PD use in older patients.

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)0.005
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.046
GPT teacher head0.371
Teacher spread0.324 · 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

Citations16
Published2015
Admission routes1
Has abstractyes

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