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Record W4399352648 · doi:10.1093/ckj/sfae076

The integrated care model: facilitating initiation of or transition to home dialysis

2024· article· en· W4399352648 on OpenAlexaff
Krishna Poinen, Sandip Mitra, Robert R. Quinn

Bibliographic record

VenueClinical Kidney Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsSouth Health CampusUniversity of CalgaryVancouver Biotech (Canada)University of British Columbia
FundersBaxter Healthcare CorporationNational Institute for Health and Care Research
KeywordsTransitional careDialysisTransition (genetics)Intensive care medicineMedicineProcess managementBusinessChemistryInternal medicinePolitical scienceHealth care

Abstract

fetched live from OpenAlex

A proportion of end-stage kidney disease (ESKD) patients require kidney replacement therapy to maintain clinical stability. Home dialysis therapies offer convenience, autonomy and potential quality of life improvements, all of which were heightened during the COVID-19 pandemic. While the superiority of specific modalities remains uncertain, patient choice and informed decision-making remain crucial. Missed opportunities for home therapies arise from systemic, programmatic and patient-level barriers. This paper introduces the integrated care model which prioritizes the safe and effective uptake of home therapies while also emphasizing patient-centered care, informed decision-making, and comprehensive support. The integrated care framework addresses challenges in patient identification, assessment, eligibility determination, education and modality transitions. Special considerations for urgent dialysis starts are discussed, acknowledging the unique barriers faced by this population. Continuous quality improvement is emphasized, with the understanding that local challenges may require tailored solutions. Overall, the integrated care model aims to create a seamless and beneficial transition to home dialysis therapies, promoting flexibility and improved quality of life for ESKD patients globally.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.378
Teacher spread0.328 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations9
Published2024
Admission routes1
Has abstractyes

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